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ARIMA</title> <meta name="description" content="Search results for: ARIMA"> <meta name="keywords" content="ARIMA"> <meta name="viewport" content="width=device-width, initial-scale=1, minimum-scale=1, maximum-scale=1, user-scalable=no"> <meta charset="utf-8"> <link href="https://cdn.waset.org/favicon.ico" type="image/x-icon" rel="shortcut icon"> <link href="https://cdn.waset.org/static/plugins/bootstrap-4.2.1/css/bootstrap.min.css" rel="stylesheet"> <link href="https://cdn.waset.org/static/plugins/fontawesome/css/all.min.css" rel="stylesheet"> <link href="https://cdn.waset.org/static/css/site.css?v=150220211555" rel="stylesheet"> </head> <body> <header> <div class="container"> <nav class="navbar navbar-expand-lg navbar-light"> <a class="navbar-brand" href="https://waset.org"> <img src="https://cdn.waset.org/static/images/wasetc.png" alt="Open Science Research Excellence" title="Open Science Research Excellence" /> </a> <button class="d-block d-lg-none navbar-toggler ml-auto" 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class="input-group"> <i class="fas fa-search"></i> <input type="text" class="search-query" name="q" placeholder="Author, Title, Abstract, Keywords" value="ARIMA"> <input type="submit" class="btn_search" value="Search"> </div> </div> </form> </div> </div> <div class="row mt-3"> <div class="col-sm-3"> <div class="card"> <div class="card-body"><strong>Commenced</strong> in January 2007</div> </div> </div> <div class="col-sm-3"> <div class="card"> <div class="card-body"><strong>Frequency:</strong> Monthly</div> </div> </div> <div class="col-sm-3"> <div class="card"> <div class="card-body"><strong>Edition:</strong> International</div> </div> </div> <div class="col-sm-3"> <div class="card"> <div class="card-body"><strong>Paper Count:</strong> 50</div> </div> </div> </div> <h1 class="mt-3 mb-3 text-center" style="font-size:1.6rem;">Search results for: ARIMA</h1> <div class="card paper-listing mb-3 mt-3"> <h5 class="card-header" style="font-size:.9rem"><span class="badge badge-info">50</span> A Stepwise Approach to Automate the Search for Optimal Parameters in Seasonal ARIMA Models</h5> <div class="card-body"> <p class="card-text"><strong>Authors:</strong> <a href="https://publications.waset.org/abstracts/search?q=Manisha%20Mukherjee">Manisha Mukherjee</a>, <a href="https://publications.waset.org/abstracts/search?q=Diptarka%20Saha"> Diptarka Saha</a> </p> <p class="card-text"><strong>Abstract:</strong></p> Reliable forecasts of univariate time series data are often necessary for several contexts. ARIMA models are quite popular among practitioners in this regard. Hence, choosing correct parameter values for ARIMA is a challenging yet imperative task. Thus, a stepwise algorithm is introduced to provide automatic and robust estimates for parameters (p; d; q)(P; D; Q) used in seasonal ARIMA models. This process is focused on improvising the overall quality of the estimates, and it alleviates the problems induced due to the unidimensional nature of the methods that are currently used such as auto.arima. The fast and automated search of parameter space also ensures reliable estimates of the parameters that possess several desirable qualities, consequently, resulting in higher test accuracy especially in the cases of noisy data. After vigorous testing on real as well as simulated data, the algorithm doesn’t only perform better than current state-of-the-art methods, it also completely obviates the need for human intervention due to its automated nature. <p class="card-text"><strong>Keywords:</strong> <a href="https://publications.waset.org/abstracts/search?q=time%20series" title="time series">time series</a>, <a href="https://publications.waset.org/abstracts/search?q=ARIMA" title=" ARIMA"> ARIMA</a>, <a href="https://publications.waset.org/abstracts/search?q=auto.arima" title=" auto.arima"> auto.arima</a>, <a href="https://publications.waset.org/abstracts/search?q=ARIMA%20parameters" title=" ARIMA parameters"> ARIMA parameters</a>, <a href="https://publications.waset.org/abstracts/search?q=forecast" title=" forecast"> forecast</a>, <a href="https://publications.waset.org/abstracts/search?q=R%20function" title=" R function"> R function</a> </p> <a href="https://publications.waset.org/abstracts/104469/a-stepwise-approach-to-automate-the-search-for-optimal-parameters-in-seasonal-arima-models" class="btn btn-primary btn-sm">Procedia</a> <a href="https://publications.waset.org/abstracts/104469.pdf" target="_blank" class="btn btn-primary btn-sm">PDF</a> <span class="bg-info text-light px-1 py-1 float-right rounded"> Downloads <span class="badge badge-light">165</span> </span> </div> </div> <div class="card paper-listing mb-3 mt-3"> <h5 class="card-header" style="font-size:.9rem"><span class="badge badge-info">49</span> ARIMA-GARCH, A Statistical Modeling for Epileptic Seizure Prediction</h5> <div class="card-body"> <p class="card-text"><strong>Authors:</strong> <a href="https://publications.waset.org/abstracts/search?q=Salman%20Mohamadi">Salman Mohamadi</a>, <a href="https://publications.waset.org/abstracts/search?q=Seyed%20Mohammad%20Ali%20Tayaranian%20Hosseini"> Seyed Mohammad Ali Tayaranian Hosseini</a>, <a href="https://publications.waset.org/abstracts/search?q=Hamidreza%20Amindavar"> Hamidreza Amindavar</a> </p> <p class="card-text"><strong>Abstract:</strong></p> In this paper, we provide a procedure to analyze and model EEG (electroencephalogram) signal as a time series using ARIMA-GARCH to predict an epileptic attack. The heteroskedasticity of EEG signal is examined through the ARCH or GARCH, (Autore- gressive conditional heteroskedasticity, Generalized autoregressive conditional heteroskedasticity) test. The best ARIMA-GARCH model in AIC sense is utilized to measure the volatility of the EEG from epileptic canine subjects, to forecast the future values of EEG. ARIMA-only model can perform prediction, but the ARCH or GARCH model acting on the residuals of ARIMA attains a con- siderable improved forecast horizon. First, we estimate the best ARIMA model, then different orders of ARCH and GARCH modelings are surveyed to determine the best heteroskedastic model of the residuals of the mentioned ARIMA. Using the simulated conditional variance of selected ARCH or GARCH model, we suggest the procedure to predict the oncoming seizures. The results indicate that GARCH modeling determines the dynamic changes of variance well before the onset of seizure. It can be inferred that the prediction capability comes from the ability of the combined ARIMA-GARCH modeling to cover the heteroskedastic nature of EEG signal changes. <p class="card-text"><strong>Keywords:</strong> <a href="https://publications.waset.org/abstracts/search?q=epileptic%20seizure%20prediction" title="epileptic seizure prediction ">epileptic seizure prediction </a>, <a href="https://publications.waset.org/abstracts/search?q=ARIMA" title=" ARIMA"> ARIMA</a>, <a href="https://publications.waset.org/abstracts/search?q=ARCH%20and%20GARCH%20modeling" title=" ARCH and GARCH modeling"> ARCH and GARCH modeling</a>, <a href="https://publications.waset.org/abstracts/search?q=heteroskedasticity" title=" heteroskedasticity"> heteroskedasticity</a>, <a href="https://publications.waset.org/abstracts/search?q=EEG" title=" EEG"> EEG</a> </p> <a href="https://publications.waset.org/abstracts/59028/arima-garch-a-statistical-modeling-for-epileptic-seizure-prediction" class="btn btn-primary btn-sm">Procedia</a> <a href="https://publications.waset.org/abstracts/59028.pdf" target="_blank" class="btn btn-primary btn-sm">PDF</a> <span class="bg-info text-light px-1 py-1 float-right rounded"> Downloads <span class="badge badge-light">406</span> </span> </div> </div> <div class="card paper-listing mb-3 mt-3"> <h5 class="card-header" style="font-size:.9rem"><span class="badge badge-info">48</span> A Comparative Analysis of ARIMA and Threshold Autoregressive Models on Exchange Rate</h5> <div class="card-body"> <p class="card-text"><strong>Authors:</strong> <a href="https://publications.waset.org/abstracts/search?q=Diteboho%20Xaba">Diteboho Xaba</a>, <a href="https://publications.waset.org/abstracts/search?q=Kolentino%20Mpeta"> Kolentino Mpeta</a>, <a href="https://publications.waset.org/abstracts/search?q=Tlotliso%20Qejoe"> Tlotliso Qejoe</a> </p> <p class="card-text"><strong>Abstract:</strong></p> This paper assesses the in-sample forecasting of the South African exchange rates comparing a linear ARIMA model and a SETAR model. The study uses a monthly adjusted data of South African exchange rates with 420 observations. Akaike information criterion (AIC) and the Schwarz information criteria (SIC) are used for model selection. Mean absolute error (MAE), root mean squared error (RMSE) and mean absolute percentage error (MAPE) are error metrics used to evaluate forecast capability of the models. The Diebold –Mariano (DM) test is employed in the study to check forecast accuracy in order to distinguish the forecasting performance between the two models (ARIMA and SETAR). The results indicate that both models perform well when modelling and forecasting the exchange rates, but SETAR seemed to outperform ARIMA. <p class="card-text"><strong>Keywords:</strong> <a href="https://publications.waset.org/abstracts/search?q=ARIMA" title="ARIMA">ARIMA</a>, <a href="https://publications.waset.org/abstracts/search?q=error%20metrices" title=" error metrices"> error metrices</a>, <a href="https://publications.waset.org/abstracts/search?q=model%20selection" title=" model selection"> model selection</a>, <a href="https://publications.waset.org/abstracts/search?q=SETAR" title=" SETAR"> SETAR</a> </p> <a href="https://publications.waset.org/abstracts/57052/a-comparative-analysis-of-arima-and-threshold-autoregressive-models-on-exchange-rate" class="btn btn-primary btn-sm">Procedia</a> <a href="https://publications.waset.org/abstracts/57052.pdf" target="_blank" class="btn btn-primary btn-sm">PDF</a> <span class="bg-info text-light px-1 py-1 float-right rounded"> Downloads <span class="badge badge-light">244</span> </span> </div> </div> <div class="card paper-listing mb-3 mt-3"> <h5 class="card-header" style="font-size:.9rem"><span class="badge badge-info">47</span> Study of ANFIS and ARIMA Model for Weather Forecasting</h5> <div class="card-body"> <p class="card-text"><strong>Authors:</strong> <a href="https://publications.waset.org/abstracts/search?q=Bandreddy%20Anand%20Babu">Bandreddy Anand Babu</a>, <a href="https://publications.waset.org/abstracts/search?q=Srinivasa%20Rao%20Mandadi"> Srinivasa Rao Mandadi</a>, <a href="https://publications.waset.org/abstracts/search?q=C.%20Pradeep%20Reddy"> C. Pradeep Reddy</a>, <a href="https://publications.waset.org/abstracts/search?q=N.%20Ramesh%20Babu"> N. Ramesh Babu</a> </p> <p class="card-text"><strong>Abstract:</strong></p> In this paper quickly illustrate the correlation investigation of Auto-Regressive Integrated Moving and Average (ARIMA) and daptive Network Based Fuzzy Inference System (ANFIS) models done by climate estimating. The climate determining is taken from University of Waterloo. The information is taken as Relative Humidity, Ambient Air Temperature, Barometric Pressure and Wind Direction utilized within this paper. The paper is carried out by analyzing the exhibitions are seen by demonstrating of ARIMA and ANIFIS model like with Sum of average of errors. Versatile Network Based Fuzzy Inference System (ANFIS) demonstrating is carried out by Mat lab programming and Auto-Regressive Integrated Moving and Average (ARIMA) displaying is produced by utilizing XLSTAT programming. ANFIS is carried out in Fuzzy Logic Toolbox in Mat Lab programming. <p class="card-text"><strong>Keywords:</strong> <a href="https://publications.waset.org/abstracts/search?q=ARIMA" title="ARIMA">ARIMA</a>, <a href="https://publications.waset.org/abstracts/search?q=ANFIS" title=" ANFIS"> ANFIS</a>, <a href="https://publications.waset.org/abstracts/search?q=fuzzy%20surmising%20tool%20stash" title=" fuzzy surmising tool stash"> fuzzy surmising tool stash</a>, <a href="https://publications.waset.org/abstracts/search?q=weather%20forecasting" title=" weather forecasting"> weather forecasting</a>, <a href="https://publications.waset.org/abstracts/search?q=MATLAB" title=" MATLAB"> MATLAB</a> </p> <a href="https://publications.waset.org/abstracts/13743/study-of-anfis-and-arima-model-for-weather-forecasting" class="btn btn-primary btn-sm">Procedia</a> <a href="https://publications.waset.org/abstracts/13743.pdf" target="_blank" class="btn btn-primary btn-sm">PDF</a> <span class="bg-info text-light px-1 py-1 float-right rounded"> Downloads <span class="badge badge-light">418</span> </span> </div> </div> <div class="card paper-listing mb-3 mt-3"> <h5 class="card-header" style="font-size:.9rem"><span class="badge badge-info">46</span> Flood Predicting in Karkheh River Basin Using Stochastic ARIMA Model</h5> <div class="card-body"> <p class="card-text"><strong>Authors:</strong> <a href="https://publications.waset.org/abstracts/search?q=Karim%20Hamidi%20Machekposhti">Karim Hamidi Machekposhti</a>, <a href="https://publications.waset.org/abstracts/search?q=Hossein%20Sedghi"> Hossein Sedghi</a>, <a href="https://publications.waset.org/abstracts/search?q=Abdolrasoul%20Telvari"> Abdolrasoul Telvari</a>, <a href="https://publications.waset.org/abstracts/search?q=Hossein%20Babazadeh"> Hossein Babazadeh</a> </p> <p class="card-text"><strong>Abstract:</strong></p> Floods have huge environmental and economic impact. Therefore, flood prediction is given a lot of attention due to its importance. This study analysed the annual maximum streamflow (discharge) (AMS or AMD) of Karkheh River in Karkheh River Basin for flood predicting using ARIMA model. For this purpose, we use the Box-Jenkins approach, which contains four-stage method model identification, parameter estimation, diagnostic checking and forecasting (predicting). The main tool used in ARIMA modelling was the SAS and SPSS software. Model identification was done by visual inspection on the ACF and PACF. SAS software computed the model parameters using the ML, CLS and ULS methods. The diagnostic checking tests, AIC criterion, RACF graph and RPACF graphs, were used for selected model verification. In this study, the best ARIMA models for Annual Maximum Discharge (AMD) time series was (4,1,1) with their AIC value of 88.87. The RACF and RPACF showed residuals’ independence. To forecast AMD for 10 future years, this model showed the ability of the model to predict floods of the river under study in the Karkheh River Basin. Model accuracy was checked by comparing the predicted and observation series by using coefficient of determination (R<sup>2</sup>). <p class="card-text"><strong>Keywords:</strong> <a href="https://publications.waset.org/abstracts/search?q=time%20series%20modelling" title="time series modelling">time series modelling</a>, <a href="https://publications.waset.org/abstracts/search?q=stochastic%20processes" title=" stochastic processes"> stochastic processes</a>, <a href="https://publications.waset.org/abstracts/search?q=ARIMA%20model" title=" ARIMA model"> ARIMA model</a>, <a href="https://publications.waset.org/abstracts/search?q=Karkheh%20river" title=" Karkheh river"> Karkheh river</a> </p> <a href="https://publications.waset.org/abstracts/76660/flood-predicting-in-karkheh-river-basin-using-stochastic-arima-model" class="btn btn-primary btn-sm">Procedia</a> <a href="https://publications.waset.org/abstracts/76660.pdf" target="_blank" class="btn btn-primary btn-sm">PDF</a> <span class="bg-info text-light px-1 py-1 float-right rounded"> Downloads <span class="badge badge-light">287</span> </span> </div> </div> <div class="card paper-listing mb-3 mt-3"> <h5 class="card-header" style="font-size:.9rem"><span class="badge badge-info">45</span> Forecasting Model for Rainfall in Thailand: Case Study Nakhon Ratchasima Province</h5> <div class="card-body"> <p class="card-text"><strong>Authors:</strong> <a href="https://publications.waset.org/abstracts/search?q=N.%20Sopipan">N. Sopipan</a> </p> <p class="card-text"><strong>Abstract:</strong></p> In this paper, we study of rainfall time series of weather stations in Nakhon Ratchasima province in Thailand using various statistical methods enabled to analyse the behaviour of rainfall in the study areas. Time-series analysis is an important tool in modelling and forecasting rainfall. ARIMA and Holt-Winter models based on exponential smoothing were built. All the models proved to be adequate. Therefore, could give information that can help decision makers establish strategies for proper planning of agriculture, drainage system and other water resource applications in Nakhon Ratchasima province. We found the best perform for forecasting is ARIMA(1,0,1)(1,0,1)12. <p class="card-text"><strong>Keywords:</strong> <a href="https://publications.waset.org/abstracts/search?q=ARIMA%20Models" title="ARIMA Models">ARIMA Models</a>, <a href="https://publications.waset.org/abstracts/search?q=exponential%20smoothing" title=" exponential smoothing"> exponential smoothing</a>, <a href="https://publications.waset.org/abstracts/search?q=Holt-Winter%20model" title=" Holt-Winter model"> Holt-Winter model</a> </p> <a href="https://publications.waset.org/abstracts/14834/forecasting-model-for-rainfall-in-thailand-case-study-nakhon-ratchasima-province" class="btn btn-primary btn-sm">Procedia</a> <a href="https://publications.waset.org/abstracts/14834.pdf" target="_blank" class="btn btn-primary btn-sm">PDF</a> <span class="bg-info text-light px-1 py-1 float-right rounded"> Downloads <span class="badge badge-light">300</span> </span> </div> </div> <div class="card paper-listing mb-3 mt-3"> <h5 class="card-header" style="font-size:.9rem"><span class="badge badge-info">44</span> Exchange Rate Forecasting by Econometric Models</h5> <div class="card-body"> <p class="card-text"><strong>Authors:</strong> <a href="https://publications.waset.org/abstracts/search?q=Zahid%20Ahmad">Zahid Ahmad</a>, <a href="https://publications.waset.org/abstracts/search?q=Nosheen%20Imran"> Nosheen Imran</a>, <a href="https://publications.waset.org/abstracts/search?q=Nauman%20Ali"> Nauman Ali</a>, <a href="https://publications.waset.org/abstracts/search?q=Farah%20Amir"> Farah Amir</a> </p> <p class="card-text"><strong>Abstract:</strong></p> The objective of the study is to forecast the US Dollar and Pak Rupee exchange rate by using time series models. For this purpose, daily exchange rates of US and Pakistan for the period of January 01, 2007 - June 2, 2017, are employed. The data set is divided into in sample and out of sample data set where in-sample data are used to estimate as well as forecast the models, whereas out-of-sample data set is exercised to forecast the exchange rate. The ADF test and PP test are used to make the time series stationary. To forecast the exchange rate ARIMA model and GARCH model are applied. Among the different Autoregressive Integrated Moving Average (ARIMA) models best model is selected on the basis of selection criteria. Due to the volatility clustering and ARCH effect the GARCH (1, 1) is also applied. Results of analysis showed that ARIMA (0, 1, 1 ) and GARCH (1, 1) are the most suitable models to forecast the future exchange rate. Further the GARCH (1,1) model provided the volatility with non-constant conditional variance in the exchange rate with good forecasting performance. This study is very useful for researchers, policymakers, and businesses for making decisions through accurate and timely forecasting of the exchange rate and helps them in devising their policies. <p class="card-text"><strong>Keywords:</strong> <a href="https://publications.waset.org/abstracts/search?q=exchange%20rate" title="exchange rate">exchange rate</a>, <a href="https://publications.waset.org/abstracts/search?q=ARIMA" title=" ARIMA"> ARIMA</a>, <a href="https://publications.waset.org/abstracts/search?q=GARCH" title=" GARCH"> GARCH</a>, <a href="https://publications.waset.org/abstracts/search?q=PAK%2FUSD" title=" PAK/USD"> PAK/USD</a> </p> <a href="https://publications.waset.org/abstracts/75639/exchange-rate-forecasting-by-econometric-models" class="btn btn-primary btn-sm">Procedia</a> <a href="https://publications.waset.org/abstracts/75639.pdf" target="_blank" class="btn btn-primary btn-sm">PDF</a> <span class="bg-info text-light px-1 py-1 float-right rounded"> Downloads <span class="badge badge-light">561</span> </span> </div> </div> <div class="card paper-listing mb-3 mt-3"> <h5 class="card-header" style="font-size:.9rem"><span class="badge badge-info">43</span> Applying Arima Data Mining Techniques to ERP to Generate Sales Demand Forecasting: A Case Study</h5> <div class="card-body"> <p class="card-text"><strong>Authors:</strong> <a href="https://publications.waset.org/abstracts/search?q=Ghaleb%20Y.%20Abbasi">Ghaleb Y. Abbasi</a>, <a href="https://publications.waset.org/abstracts/search?q=Israa%20Abu%20Rumman"> Israa Abu Rumman</a> </p> <p class="card-text"><strong>Abstract:</strong></p> This paper modeled sales history archived from 2012 to 2015 bulked in monthly bins for five products for a medical supply company in Jordan. The sales forecasts and extracted consistent patterns in the sales demand history from the Enterprise Resource Planning (ERP) system were used to predict future forecasting and generate sales demand forecasting using time series analysis statistical technique called Auto Regressive Integrated Moving Average (ARIMA). This was used to model and estimate realistic sales demand patterns and predict future forecasting to decide the best models for five products. Analysis revealed that the current replenishment system indicated inventory overstocking. <p class="card-text"><strong>Keywords:</strong> <a href="https://publications.waset.org/abstracts/search?q=ARIMA%20models" title="ARIMA models">ARIMA models</a>, <a href="https://publications.waset.org/abstracts/search?q=sales%20demand%20forecasting" title=" sales demand forecasting"> sales demand forecasting</a>, <a href="https://publications.waset.org/abstracts/search?q=time%20series" title=" time series"> time series</a>, <a href="https://publications.waset.org/abstracts/search?q=R%20code" title=" R code"> R code</a> </p> <a href="https://publications.waset.org/abstracts/64117/applying-arima-data-mining-techniques-to-erp-to-generate-sales-demand-forecasting-a-case-study" class="btn btn-primary btn-sm">Procedia</a> <a href="https://publications.waset.org/abstracts/64117.pdf" target="_blank" class="btn btn-primary btn-sm">PDF</a> <span class="bg-info text-light px-1 py-1 float-right rounded"> Downloads <span class="badge badge-light">385</span> </span> </div> </div> <div class="card paper-listing mb-3 mt-3"> <h5 class="card-header" style="font-size:.9rem"><span class="badge badge-info">42</span> Application of Stochastic Models to Annual Extreme Streamflow Data</h5> <div class="card-body"> <p class="card-text"><strong>Authors:</strong> <a href="https://publications.waset.org/abstracts/search?q=Karim%20Hamidi%20Machekposhti">Karim Hamidi Machekposhti</a>, <a href="https://publications.waset.org/abstracts/search?q=Hossein%20Sedghi"> Hossein Sedghi</a> </p> <p class="card-text"><strong>Abstract:</strong></p> This study was designed to find the best stochastic model (using of time series analysis) for annual extreme streamflow (peak and maximum streamflow) of Karkheh River at Iran. The Auto-regressive Integrated Moving Average (ARIMA) model used to simulate these series and forecast those in future. For the analysis, annual extreme streamflow data of Jelogir Majin station (above of Karkheh dam reservoir) for the years 1958–2005 were used. A visual inspection of the time plot gives a little increasing trend; therefore, series is not stationary. The stationarity observed in Auto-Correlation Function (ACF) and Partial Auto-Correlation Function (PACF) plots of annual extreme streamflow was removed using first order differencing (d=1) in order to the development of the ARIMA model. Interestingly, the ARIMA(4,1,1) model developed was found to be most suitable for simulating annual extreme streamflow for Karkheh River. The model was found to be appropriate to forecast ten years of annual extreme streamflow and assist decision makers to establish priorities for water demand. The Statistical Analysis System (SAS) and Statistical Package for the Social Sciences (SPSS) codes were used to determinate of the best model for this series. <p class="card-text"><strong>Keywords:</strong> <a href="https://publications.waset.org/abstracts/search?q=stochastic%20models" title="stochastic models">stochastic models</a>, <a href="https://publications.waset.org/abstracts/search?q=ARIMA" title=" ARIMA"> ARIMA</a>, <a href="https://publications.waset.org/abstracts/search?q=extreme%20streamflow" title=" extreme streamflow"> extreme streamflow</a>, <a href="https://publications.waset.org/abstracts/search?q=Karkheh%20river" title=" Karkheh river"> Karkheh river</a> </p> <a href="https://publications.waset.org/abstracts/97759/application-of-stochastic-models-to-annual-extreme-streamflow-data" class="btn btn-primary btn-sm">Procedia</a> <a href="https://publications.waset.org/abstracts/97759.pdf" target="_blank" class="btn btn-primary btn-sm">PDF</a> <span class="bg-info text-light px-1 py-1 float-right rounded"> Downloads <span class="badge badge-light">148</span> </span> </div> </div> <div class="card paper-listing mb-3 mt-3"> <h5 class="card-header" style="font-size:.9rem"><span class="badge badge-info">41</span> Time Series Modelling and Prediction of River Runoff: Case Study of Karkheh River, Iran</h5> <div class="card-body"> <p class="card-text"><strong>Authors:</strong> <a href="https://publications.waset.org/abstracts/search?q=Karim%20Hamidi%20Machekposhti">Karim Hamidi Machekposhti</a>, <a href="https://publications.waset.org/abstracts/search?q=Hossein%20Sedghi"> Hossein Sedghi</a>, <a href="https://publications.waset.org/abstracts/search?q=Abdolrasoul%20Telvari"> Abdolrasoul Telvari</a>, <a href="https://publications.waset.org/abstracts/search?q=Hossein%20Babazadeh"> Hossein Babazadeh</a> </p> <p class="card-text"><strong>Abstract:</strong></p> Rainfall and runoff phenomenon is a chaotic and complex outcome of nature which requires sophisticated modelling and simulation methods for explanation and use. Time Series modelling allows runoff data analysis and can be used as forecasting tool. In the paper attempt is made to model river runoff data and predict the future behavioural pattern of river based on annual past observations of annual river runoff. The river runoff analysis and predict are done using ARIMA model. For evaluating the efficiency of prediction to hydrological events such as rainfall, runoff and etc., we use the statistical formulae applicable. The good agreement between predicted and observation river runoff coefficient of determination (R<sup>2</sup>) display that the ARIMA (4,1,1) is the suitable model for predicting Karkheh River runoff at Iran. <p class="card-text"><strong>Keywords:</strong> <a href="https://publications.waset.org/abstracts/search?q=time%20series%20modelling" title="time series modelling">time series modelling</a>, <a href="https://publications.waset.org/abstracts/search?q=ARIMA%20model" title=" ARIMA model"> ARIMA model</a>, <a href="https://publications.waset.org/abstracts/search?q=river%20runoff" title=" river runoff"> river runoff</a>, <a href="https://publications.waset.org/abstracts/search?q=Karkheh%20River" title=" Karkheh River"> Karkheh River</a>, <a href="https://publications.waset.org/abstracts/search?q=CLS%20method" title=" CLS method"> CLS method</a> </p> <a href="https://publications.waset.org/abstracts/76659/time-series-modelling-and-prediction-of-river-runoff-case-study-of-karkheh-river-iran" class="btn btn-primary btn-sm">Procedia</a> <a href="https://publications.waset.org/abstracts/76659.pdf" target="_blank" class="btn btn-primary btn-sm">PDF</a> <span class="bg-info text-light px-1 py-1 float-right rounded"> Downloads <span class="badge badge-light">341</span> </span> </div> </div> <div class="card paper-listing mb-3 mt-3"> <h5 class="card-header" style="font-size:.9rem"><span class="badge badge-info">40</span> A Mobile Application for Analyzing and Forecasting Crime Using Autoregressive Integrated Moving Average with Artificial Neural Network</h5> <div class="card-body"> <p class="card-text"><strong>Authors:</strong> <a href="https://publications.waset.org/abstracts/search?q=Gajaanuja%20Megalathan">Gajaanuja Megalathan</a>, <a href="https://publications.waset.org/abstracts/search?q=Banuka%20Athuraliya"> Banuka Athuraliya</a> </p> <p class="card-text"><strong>Abstract:</strong></p> Crime is one of our society's most intimidating and threatening challenges. With the majority of the population residing in cities, many experts and data provided by local authorities suggest a rapid increase in the number of crimes committed in these cities in recent years. There has been an increasing graph in the crime rates. People living in Sri Lanka have the right to know the exact crime rates and the crime rates in the future of the place they are living in. Due to the current economic crisis, crime rates have spiked. There have been so many thefts and murders recorded within the last 6-10 months. Although there are many sources to find out, there is no solid way of searching and finding out the safety of the place. Due to all these reasons, there is a need for the public to feel safe when they are introduced to new places. Through this research, the author aims to develop a mobile application that will be a solution to this problem. It is mainly targeted at tourists, and people who recently relocated will gain advantage of this application. Moreover, the Arima Model combined with ANN is to be used to predict crime rates. From the past researchers' works, it is evidently clear that they haven’t used the Arima model combined with Artificial Neural Networks to forecast crimes. <p class="card-text"><strong>Keywords:</strong> <a href="https://publications.waset.org/abstracts/search?q=arima%20model" title="arima model">arima model</a>, <a href="https://publications.waset.org/abstracts/search?q=ANN" title=" ANN"> ANN</a>, <a href="https://publications.waset.org/abstracts/search?q=crime%20prediction" title=" crime prediction"> crime prediction</a>, <a href="https://publications.waset.org/abstracts/search?q=data%20analysis" title=" data analysis"> data analysis</a> </p> <a href="https://publications.waset.org/abstracts/169076/a-mobile-application-for-analyzing-and-forecasting-crime-using-autoregressive-integrated-moving-average-with-artificial-neural-network" class="btn btn-primary btn-sm">Procedia</a> <a href="https://publications.waset.org/abstracts/169076.pdf" target="_blank" class="btn btn-primary btn-sm">PDF</a> <span class="bg-info text-light px-1 py-1 float-right rounded"> Downloads <span class="badge badge-light">131</span> </span> </div> </div> <div class="card paper-listing mb-3 mt-3"> <h5 class="card-header" style="font-size:.9rem"><span class="badge badge-info">39</span> Design and Development of an Algorithm to Predict Fluctuations of Currency Rates</h5> <div class="card-body"> <p class="card-text"><strong>Authors:</strong> <a href="https://publications.waset.org/abstracts/search?q=Nuwan%20Kuruwitaarachchi">Nuwan Kuruwitaarachchi</a>, <a href="https://publications.waset.org/abstracts/search?q=M.%20K.%20M.%20Peiris"> M. K. M. Peiris</a>, <a href="https://publications.waset.org/abstracts/search?q=C.%20N.%20Madawala"> C. N. Madawala</a>, <a href="https://publications.waset.org/abstracts/search?q=K.%20M.%20A.%20R.%20Perera"> K. M. A. R. Perera</a>, <a href="https://publications.waset.org/abstracts/search?q=V.%20U.%20N%20Perera"> V. U. N Perera</a> </p> <p class="card-text"><strong>Abstract:</strong></p> Dealing with businesses with the foreign market always took a special place in a country’s economy. Political and social factors came into play making currency rate changes fluctuate rapidly. Currency rate prediction has become an important factor for larger international businesses since large amounts of money exchanged between countries. This research focuses on comparing the accuracy of mainly three models; Autoregressive Integrated Moving Average (ARIMA), Artificial Neural Networks(ANN) and Support Vector Machines(SVM). series of data import, export, USD currency exchange rate respect to LKR has been selected for training using above mentioned algorithms. After training the data set and comparing each algorithm, it was able to see that prediction in SVM performed better than other models. It was improved more by combining SVM and SVR models together. <p class="card-text"><strong>Keywords:</strong> <a href="https://publications.waset.org/abstracts/search?q=ARIMA" title="ARIMA">ARIMA</a>, <a href="https://publications.waset.org/abstracts/search?q=ANN" title=" ANN"> ANN</a>, <a href="https://publications.waset.org/abstracts/search?q=FFNN" title=" FFNN"> FFNN</a>, <a href="https://publications.waset.org/abstracts/search?q=RMSE" title=" RMSE"> RMSE</a>, <a href="https://publications.waset.org/abstracts/search?q=SVM" title=" SVM"> SVM</a>, <a href="https://publications.waset.org/abstracts/search?q=SVR" title=" SVR"> SVR</a> </p> <a href="https://publications.waset.org/abstracts/82496/design-and-development-of-an-algorithm-to-predict-fluctuations-of-currency-rates" class="btn btn-primary btn-sm">Procedia</a> <a href="https://publications.waset.org/abstracts/82496.pdf" target="_blank" class="btn btn-primary btn-sm">PDF</a> <span class="bg-info text-light px-1 py-1 float-right rounded"> Downloads <span class="badge badge-light">212</span> </span> </div> </div> <div class="card paper-listing mb-3 mt-3"> <h5 class="card-header" style="font-size:.9rem"><span class="badge badge-info">38</span> Statistical Time-Series and Neural Architecture of Malaria Patients Records in Lagos, Nigeria</h5> <div class="card-body"> <p class="card-text"><strong>Authors:</strong> <a href="https://publications.waset.org/abstracts/search?q=Akinbo%20Razak%20Yinka">Akinbo Razak Yinka</a>, <a href="https://publications.waset.org/abstracts/search?q=Adesanya%20Kehinde%20Kazeem"> Adesanya Kehinde Kazeem</a>, <a href="https://publications.waset.org/abstracts/search?q=Oladokun%20Oluwagbenga%20Peter"> Oladokun Oluwagbenga Peter</a> </p> <p class="card-text"><strong>Abstract:</strong></p> Time series data are sequences of observations collected over a period of time. Such data can be used to predict health outcomes, such as disease progression, mortality, hospitalization, etc. The Statistical approach is based on mathematical models that capture the patterns and trends of the data, such as autocorrelation, seasonality, and noise, while Neural methods are based on artificial neural networks, which are computational models that mimic the structure and function of biological neurons. This paper compared both parametric and non-parametric time series models of patients treated for malaria in Maternal and Child Health Centres in Lagos State, Nigeria. The forecast methods considered linear regression, Integrated Moving Average, ARIMA and SARIMA Modeling for the parametric approach, while Multilayer Perceptron (MLP) and Long Short-Term Memory (LSTM) Network were used for the non-parametric model. The performance of each method is evaluated using the Mean Absolute Error (MAE), R-squared (R2) and Root Mean Square Error (RMSE) as criteria to determine the accuracy of each model. The study revealed that the best performance in terms of error was found in MLP, followed by the LSTM and ARIMA models. In addition, the Bootstrap Aggregating technique was used to make robust forecasts when there are uncertainties in the data. <p class="card-text"><strong>Keywords:</strong> <a href="https://publications.waset.org/abstracts/search?q=ARIMA" title="ARIMA">ARIMA</a>, <a href="https://publications.waset.org/abstracts/search?q=bootstrap%20aggregation" title=" bootstrap aggregation"> bootstrap aggregation</a>, <a href="https://publications.waset.org/abstracts/search?q=MLP" title=" MLP"> MLP</a>, <a href="https://publications.waset.org/abstracts/search?q=LSTM" title=" LSTM"> LSTM</a>, <a href="https://publications.waset.org/abstracts/search?q=SARIMA" title=" SARIMA"> SARIMA</a>, <a href="https://publications.waset.org/abstracts/search?q=time-series%20analysis" title=" time-series analysis"> time-series analysis</a> </p> <a href="https://publications.waset.org/abstracts/176559/statistical-time-series-and-neural-architecture-of-malaria-patients-records-in-lagos-nigeria" class="btn btn-primary btn-sm">Procedia</a> <a href="https://publications.waset.org/abstracts/176559.pdf" target="_blank" class="btn btn-primary btn-sm">PDF</a> <span class="bg-info text-light px-1 py-1 float-right rounded"> Downloads <span class="badge badge-light">75</span> </span> </div> </div> <div class="card paper-listing mb-3 mt-3"> <h5 class="card-header" style="font-size:.9rem"><span class="badge badge-info">37</span> Analysis and Prediction of Fine Particulate Matter in the Air Environment for 2007-2020 in Bangkok Thailand</h5> <div class="card-body"> <p class="card-text"><strong>Authors:</strong> <a href="https://publications.waset.org/abstracts/search?q=Phawichsak%20Prapassornpitaya">Phawichsak Prapassornpitaya</a>, <a href="https://publications.waset.org/abstracts/search?q=Wanida%20Jinsart"> Wanida Jinsart</a> </p> <p class="card-text"><strong>Abstract:</strong></p> Daily monitoring PM₁₀ and PM₂.₅ data from 2007 to 2017 were analyzed to provide baseline data for prediction of the air pollution in Bangkok in the period of 2018 -2020. Two statistical models, Autoregressive Integrated Moving Average model (ARIMA) were used to evaluate the trends of pollutions. The prediction concentrations were tested by root means square error (RMSE) and index of agreement (IOA). This evaluation of the traffic PM₂.₅ and PM₁₀ were studied in association with the regulatory control and emission standard changes. The emission factors of particulate matter from diesel vehicles were decreased when applied higher number of euro standard. The trends of ambient air pollutions were expected to decrease. However, the Bangkok smog episode in February 2018 with temperature inversion caused high concentration of PM₂.₅ in the air environment of Bangkok. The impact of traffic pollutants was depended upon the emission sources, temperature variations, and metrological conditions. <p class="card-text"><strong>Keywords:</strong> <a href="https://publications.waset.org/abstracts/search?q=fine%20particulate%20matter" title="fine particulate matter">fine particulate matter</a>, <a href="https://publications.waset.org/abstracts/search?q=ARIMA" title=" ARIMA"> ARIMA</a>, <a href="https://publications.waset.org/abstracts/search?q=RMSE" title=" RMSE"> RMSE</a>, <a href="https://publications.waset.org/abstracts/search?q=Bangkok" title=" Bangkok"> Bangkok</a> </p> <a href="https://publications.waset.org/abstracts/90925/analysis-and-prediction-of-fine-particulate-matter-in-the-air-environment-for-2007-2020-in-bangkok-thailand" class="btn btn-primary btn-sm">Procedia</a> <a href="https://publications.waset.org/abstracts/90925.pdf" target="_blank" class="btn btn-primary btn-sm">PDF</a> <span class="bg-info text-light px-1 py-1 float-right rounded"> Downloads <span class="badge badge-light">278</span> </span> </div> </div> <div class="card paper-listing mb-3 mt-3"> <h5 class="card-header" style="font-size:.9rem"><span class="badge badge-info">36</span> Monthly River Flow Prediction Using a Nonlinear Prediction Method</h5> <div class="card-body"> <p class="card-text"><strong>Authors:</strong> <a href="https://publications.waset.org/abstracts/search?q=N.%20H.%20Adenan">N. H. Adenan</a>, <a href="https://publications.waset.org/abstracts/search?q=M.%20S.%20M.%20Noorani"> M. S. M. Noorani</a> </p> <p class="card-text"><strong>Abstract:</strong></p> River flow prediction is an essential to ensure proper management of water resources can be optimally distribute water to consumers. This study presents an analysis and prediction by using nonlinear prediction method involving monthly river flow data in Tanjung Tualang from 1976 to 2006. Nonlinear prediction method involves the reconstruction of phase space and local linear approximation approach. The phase space reconstruction involves the reconstruction of one-dimensional (the observed 287 months of data) in a multidimensional phase space to reveal the dynamics of the system. Revenue of phase space reconstruction is used to predict the next 72 months. A comparison of prediction performance based on correlation coefficient (CC) and root mean square error (RMSE) have been employed to compare prediction performance for nonlinear prediction method, ARIMA and SVM. Prediction performance comparisons show the prediction results using nonlinear prediction method is better than ARIMA and SVM. Therefore, the result of this study could be used to developed an efficient water management system to optimize the allocation water resources. <p class="card-text"><strong>Keywords:</strong> <a href="https://publications.waset.org/abstracts/search?q=river%20flow" title="river flow">river flow</a>, <a href="https://publications.waset.org/abstracts/search?q=nonlinear%20prediction%20method" title=" nonlinear prediction method"> nonlinear prediction method</a>, <a href="https://publications.waset.org/abstracts/search?q=phase%20space" title=" phase space"> phase space</a>, <a href="https://publications.waset.org/abstracts/search?q=local%20linear%20approximation" title=" local linear approximation"> local linear approximation</a> </p> <a href="https://publications.waset.org/abstracts/2867/monthly-river-flow-prediction-using-a-nonlinear-prediction-method" class="btn btn-primary btn-sm">Procedia</a> <a href="https://publications.waset.org/abstracts/2867.pdf" target="_blank" class="btn btn-primary btn-sm">PDF</a> <span class="bg-info text-light px-1 py-1 float-right rounded"> Downloads <span class="badge badge-light">412</span> </span> </div> </div> <div class="card paper-listing mb-3 mt-3"> <h5 class="card-header" style="font-size:.9rem"><span class="badge badge-info">35</span> An Approach for Pattern Recognition and Prediction of Information Diffusion Model on Twitter</h5> <div class="card-body"> <p class="card-text"><strong>Authors:</strong> <a href="https://publications.waset.org/abstracts/search?q=Amartya%20Hatua">Amartya Hatua</a>, <a href="https://publications.waset.org/abstracts/search?q=Trung%20Nguyen"> Trung Nguyen</a>, <a href="https://publications.waset.org/abstracts/search?q=Andrew%20Sung"> Andrew Sung</a> </p> <p class="card-text"><strong>Abstract:</strong></p> In this paper, we study the information diffusion process on Twitter as a multivariate time series problem. Our model concerns three measures (volume, network influence, and sentiment of tweets) based on 10 features, and we collected 27 million tweets to build our information diffusion time series dataset for analysis. Then, different time series clustering techniques with Dynamic Time Warping (DTW) distance were used to identify different patterns of information diffusion. Finally, we built the information diffusion prediction models for new hashtags which comprise two phrases: The first phrase is recognizing the pattern using k-NN with DTW distance; the second phrase is building the forecasting model using the traditional Autoregressive Integrated Moving Average (ARIMA) model and the non-linear recurrent neural network of Long Short-Term Memory (LSTM). Preliminary results of performance evaluation between different forecasting models show that LSTM with clustering information notably outperforms other models. Therefore, our approach can be applied in real-world applications to analyze and predict the information diffusion characteristics of selected topics or memes (hashtags) in Twitter. <p class="card-text"><strong>Keywords:</strong> <a href="https://publications.waset.org/abstracts/search?q=ARIMA" title="ARIMA">ARIMA</a>, <a href="https://publications.waset.org/abstracts/search?q=DTW" title=" DTW"> DTW</a>, <a href="https://publications.waset.org/abstracts/search?q=information%20diffusion" title=" information diffusion"> information diffusion</a>, <a href="https://publications.waset.org/abstracts/search?q=LSTM" title=" LSTM"> LSTM</a>, <a href="https://publications.waset.org/abstracts/search?q=RNN" title=" RNN"> RNN</a>, <a href="https://publications.waset.org/abstracts/search?q=time%20series%20clustering" title=" time series clustering"> time series clustering</a>, <a href="https://publications.waset.org/abstracts/search?q=time%20series%20forecasting" title=" time series forecasting"> time series forecasting</a>, <a href="https://publications.waset.org/abstracts/search?q=Twitter" title=" Twitter"> Twitter</a> </p> <a href="https://publications.waset.org/abstracts/80797/an-approach-for-pattern-recognition-and-prediction-of-information-diffusion-model-on-twitter" class="btn btn-primary btn-sm">Procedia</a> <a href="https://publications.waset.org/abstracts/80797.pdf" target="_blank" class="btn btn-primary btn-sm">PDF</a> <span class="bg-info text-light px-1 py-1 float-right rounded"> Downloads <span class="badge badge-light">391</span> </span> </div> </div> <div class="card paper-listing mb-3 mt-3"> <h5 class="card-header" style="font-size:.9rem"><span class="badge badge-info">34</span> A Machine Learning Approach for Anomaly Detection in Environmental IoT-Driven Wastewater Purification Systems</h5> <div class="card-body"> <p class="card-text"><strong>Authors:</strong> <a href="https://publications.waset.org/abstracts/search?q=Giovanni%20Cicceri">Giovanni Cicceri</a>, <a href="https://publications.waset.org/abstracts/search?q=Roberta%20Maisano"> Roberta Maisano</a>, <a href="https://publications.waset.org/abstracts/search?q=Nathalie%20Morey"> Nathalie Morey</a>, <a href="https://publications.waset.org/abstracts/search?q=Salvatore%20Distefano"> Salvatore Distefano</a> </p> <p class="card-text"><strong>Abstract:</strong></p> The main goal of this paper is to present a solution for a water purification system based on an Environmental Internet of Things (EIoT) platform to monitor and control water quality and machine learning (ML) models to support decision making and speed up the processes of purification of water. A real case study has been implemented by deploying an EIoT platform and a network of devices, called Gramb meters and belonging to the Gramb project, on wastewater purification systems located in Calabria, south of Italy. The data thus collected are used to control the wastewater quality, detect anomalies and predict the behaviour of the purification system. To this extent, three different statistical and machine learning models have been adopted and thus compared: Autoregressive Integrated Moving Average (ARIMA), Long Short Term Memory (LSTM) autoencoder, and Facebook Prophet (FP). The results demonstrated that the ML solution (LSTM) out-perform classical statistical approaches (ARIMA, FP), in terms of both accuracy, efficiency and effectiveness in monitoring and controlling the wastewater purification processes. <p class="card-text"><strong>Keywords:</strong> <a href="https://publications.waset.org/abstracts/search?q=environmental%20internet%20of%20things" title="environmental internet of things">environmental internet of things</a>, <a href="https://publications.waset.org/abstracts/search?q=EIoT" title=" EIoT"> EIoT</a>, <a href="https://publications.waset.org/abstracts/search?q=machine%20learning" title=" machine learning"> machine learning</a>, <a href="https://publications.waset.org/abstracts/search?q=anomaly%20detection" title=" anomaly detection"> anomaly detection</a>, <a href="https://publications.waset.org/abstracts/search?q=environment%20monitoring" title=" environment monitoring"> environment monitoring</a> </p> <a href="https://publications.waset.org/abstracts/130838/a-machine-learning-approach-for-anomaly-detection-in-environmental-iot-driven-wastewater-purification-systems" class="btn btn-primary btn-sm">Procedia</a> <a href="https://publications.waset.org/abstracts/130838.pdf" target="_blank" class="btn btn-primary btn-sm">PDF</a> <span class="bg-info text-light px-1 py-1 float-right rounded"> Downloads <span class="badge badge-light">151</span> </span> </div> </div> <div class="card paper-listing mb-3 mt-3"> <h5 class="card-header" style="font-size:.9rem"><span class="badge badge-info">33</span> Time Series Modelling for Forecasting Wheat Production and Consumption of South Africa in Time of War</h5> <div class="card-body"> <p class="card-text"><strong>Authors:</strong> <a href="https://publications.waset.org/abstracts/search?q=Yiseyon%20Hosu">Yiseyon Hosu</a>, <a href="https://publications.waset.org/abstracts/search?q=Joseph%20Akande"> Joseph Akande</a> </p> <p class="card-text"><strong>Abstract:</strong></p> Wheat is one of the most important staple food grains of human for centuries and is largely consumed in South Africa. It has a special place in the South African economy because of its significance in food security, trade, and industry. This paper modelled and forecast the production and consumption of wheat in South Africa in the time covid-19 and the ongoing Russia-Ukraine war by using annual time series data from 1940–2021 based on the ARIMA models. Both the averaging forecast and selected models forecast indicate that there is the possibility of an increase with respect to production. The minimum and maximum growth in production is projected to be between 3million and 10 million tons, respectively. However, the model also forecast a possibility of depression with respect to consumption in South Africa. Although Covid-19 and the war between Ukraine and Russia, two major producers and exporters of global wheat, are having an effect on the volatility of the prices currently, the wheat production in South African is expected to increase and meat the consumption demand and provided an opportunity for increase export with respect to domestic consumption. The forecasting of production and consumption behaviours of major crops play an important role towards food and nutrition security, these findings can assist policymakers and will provide them with insights into the production and pricing policy of wheat in South Africa. <p class="card-text"><strong>Keywords:</strong> <a href="https://publications.waset.org/abstracts/search?q=ARIMA" title="ARIMA">ARIMA</a>, <a href="https://publications.waset.org/abstracts/search?q=food%20security" title=" food security"> food security</a>, <a href="https://publications.waset.org/abstracts/search?q=price%20volatility" title=" price volatility"> price volatility</a>, <a href="https://publications.waset.org/abstracts/search?q=staple%20food" title=" staple food"> staple food</a>, <a href="https://publications.waset.org/abstracts/search?q=South%20Africa" title=" South Africa"> South Africa</a> </p> <a href="https://publications.waset.org/abstracts/153563/time-series-modelling-for-forecasting-wheat-production-and-consumption-of-south-africa-in-time-of-war" class="btn btn-primary btn-sm">Procedia</a> <a href="https://publications.waset.org/abstracts/153563.pdf" target="_blank" class="btn btn-primary btn-sm">PDF</a> <span class="bg-info text-light px-1 py-1 float-right rounded"> Downloads <span class="badge badge-light">102</span> </span> </div> </div> <div class="card paper-listing mb-3 mt-3"> <h5 class="card-header" style="font-size:.9rem"><span class="badge badge-info">32</span> Big Data in Telecom Industry: Effective Predictive Techniques on Call Detail Records</h5> <div class="card-body"> <p class="card-text"><strong>Authors:</strong> <a href="https://publications.waset.org/abstracts/search?q=Sara%20ElElimy">Sara ElElimy</a>, <a href="https://publications.waset.org/abstracts/search?q=Samir%20Moustafa"> Samir Moustafa</a> </p> <p class="card-text"><strong>Abstract:</strong></p> Mobile network operators start to face many challenges in the digital era, especially with high demands from customers. Since mobile network operators are considered a source of big data, traditional techniques are not effective with new era of big data, Internet of things (IoT) and 5G; as a result, handling effectively different big datasets becomes a vital task for operators with the continuous growth of data and moving from long term evolution (LTE) to 5G. So, there is an urgent need for effective Big data analytics to predict future demands, traffic, and network performance to full fill the requirements of the fifth generation of mobile network technology. In this paper, we introduce data science techniques using machine learning and deep learning algorithms: the autoregressive integrated moving average (ARIMA), Bayesian-based curve fitting, and recurrent neural network (RNN) are employed for a data-driven application to mobile network operators. The main framework included in models are identification parameters of each model, estimation, prediction, and final data-driven application of this prediction from business and network performance applications. These models are applied to Telecom Italia Big Data challenge call detail records (CDRs) datasets. The performance of these models is found out using a specific well-known evaluation criteria shows that ARIMA (machine learning-based model) is more accurate as a predictive model in such a dataset than the RNN (deep learning model). <p class="card-text"><strong>Keywords:</strong> <a href="https://publications.waset.org/abstracts/search?q=big%20data%20analytics" title="big data analytics">big data analytics</a>, <a href="https://publications.waset.org/abstracts/search?q=machine%20learning" title=" machine learning"> machine learning</a>, <a href="https://publications.waset.org/abstracts/search?q=CDRs" title=" CDRs"> CDRs</a>, <a href="https://publications.waset.org/abstracts/search?q=5G" title=" 5G"> 5G</a> </p> <a href="https://publications.waset.org/abstracts/115530/big-data-in-telecom-industry-effective-predictive-techniques-on-call-detail-records" class="btn btn-primary btn-sm">Procedia</a> <a href="https://publications.waset.org/abstracts/115530.pdf" target="_blank" class="btn btn-primary btn-sm">PDF</a> <span class="bg-info text-light px-1 py-1 float-right rounded"> Downloads <span class="badge badge-light">139</span> </span> </div> </div> <div class="card paper-listing mb-3 mt-3"> <h5 class="card-header" style="font-size:.9rem"><span class="badge badge-info">31</span> Forecasting Residential Water Consumption in Hamilton, New Zealand</h5> <div class="card-body"> <p class="card-text"><strong>Authors:</strong> <a href="https://publications.waset.org/abstracts/search?q=Farnaz%20Farhangi">Farnaz Farhangi</a> </p> <p class="card-text"><strong>Abstract:</strong></p> Many people in New Zealand believe that the access to water is inexhaustible, and it comes from a history of virtually unrestricted access to it. For the region like Hamilton which is one of New Zealand’s fastest growing cities, it is crucial for policy makers to know about the future water consumption and implementation of rules and regulation such as universal water metering. Hamilton residents use water freely and they do not have any idea about how much water they use. Hence, one of proposed objectives of this research is focusing on forecasting water consumption using different methods. Residential water consumption time series exhibits seasonal and trend variations. Seasonality is the pattern caused by repeating events such as weather conditions in summer and winter, public holidays, etc. The problem with this seasonal fluctuation is that, it dominates other time series components and makes difficulties in determining other variations (such as educational campaign’s effect, regulation, etc.) in time series. Apart from seasonality, a stochastic trend is also combined with seasonality and makes different effects on results of forecasting. According to the forecasting literature, preprocessing (de-trending and de-seasonalization) is essential to have more performed forecasting results, while some other researchers mention that seasonally non-adjusted data should be used. Hence, I answer the question that is pre-processing essential? A wide range of forecasting methods exists with different pros and cons. In this research, I apply double seasonal ARIMA and Artificial Neural Network (ANN), considering diverse elements such as seasonality and calendar effects (public and school holidays) and combine their results to find the best predicted values. My hypothesis is the examination the results of combined method (hybrid model) and individual methods and comparing the accuracy and robustness. In order to use ARIMA, the data should be stationary. Also, ANN has successful forecasting applications in terms of forecasting seasonal and trend time series. Using a hybrid model is a way to improve the accuracy of the methods. Due to the fact that water demand is dominated by different seasonality, in order to find their sensitivity to weather conditions or calendar effects or other seasonal patterns, I combine different methods. The advantage of this combination is reduction of errors by averaging of each individual model. It is also useful when we are not sure about the accuracy of each forecasting model and it can ease the problem of model selection. Using daily residential water consumption data from January 2000 to July 2015 in Hamilton, I indicate how prediction by different methods varies. ANN has more accurate forecasting results than other method and preprocessing is essential when we use seasonal time series. Using hybrid model reduces forecasting average errors and increases the performance. <p class="card-text"><strong>Keywords:</strong> <a href="https://publications.waset.org/abstracts/search?q=artificial%20neural%20network%20%28ANN%29" title="artificial neural network (ANN)">artificial neural network (ANN)</a>, <a href="https://publications.waset.org/abstracts/search?q=double%20seasonal%20ARIMA" title=" double seasonal ARIMA"> double seasonal ARIMA</a>, <a href="https://publications.waset.org/abstracts/search?q=forecasting" title=" forecasting"> forecasting</a>, <a href="https://publications.waset.org/abstracts/search?q=hybrid%20model" title=" hybrid model "> hybrid model </a> </p> <a href="https://publications.waset.org/abstracts/36852/forecasting-residential-water-consumption-in-hamilton-new-zealand" class="btn btn-primary btn-sm">Procedia</a> <a href="https://publications.waset.org/abstracts/36852.pdf" target="_blank" class="btn btn-primary btn-sm">PDF</a> <span class="bg-info text-light px-1 py-1 float-right rounded"> Downloads <span class="badge badge-light">337</span> </span> </div> </div> <div class="card paper-listing mb-3 mt-3"> <h5 class="card-header" style="font-size:.9rem"><span class="badge badge-info">30</span> Design of a Standard Weather Data Acquisition Device for the Federal University of Technology, Akure Nigeria</h5> <div class="card-body"> <p class="card-text"><strong>Authors:</strong> <a href="https://publications.waset.org/abstracts/search?q=Isaac%20Kayode%20Ogunlade">Isaac Kayode Ogunlade</a> </p> <p class="card-text"><strong>Abstract:</strong></p> Data acquisition (DAQ) is the process by which physical phenomena from the real world are transformed into an electrical signal(s) that are measured and converted into a digital format for processing, analysis, and storage by a computer. The DAQ is designed using PIC18F4550 microcontroller, communicating with Personal Computer (PC) through USB (Universal Serial Bus). The research deployed initial knowledge of data acquisition system and embedded system to develop a weather data acquisition device using LM35 sensor to measure weather parameters and the use of Artificial Intelligence(Artificial Neural Network - ANN)and statistical approach(Autoregressive Integrated Moving Average – ARIMA) to predict precipitation (rainfall). The device is placed by a standard device in the Department of Meteorology, Federal University of Technology, Akure (FUTA) to know the performance evaluation of the device. Both devices (standard and designed) were subjected to 180 days with the same atmospheric condition for data mining (temperature, relative humidity, and pressure). The acquired data is trained in MATLAB R2012b environment using ANN, and ARIMAto predict precipitation (rainfall). Root Mean Square Error (RMSE), Mean Absolute Error (MAE), Correction Square (R2), and Mean Percentage Error (MPE) was deplored as standardize evaluation to know the performance of the models in the prediction of precipitation. The results from the working of the developed device show that the device has an efficiency of 96% and is also compatible with Personal Computer (PC) and laptops. The simulation result for acquired data shows that ANN models precipitation (rainfall) prediction for two months (May and June 2017) revealed a disparity error of 1.59%; while ARIMA is 2.63%, respectively. The device will be useful in research, practical laboratories, and industrial environments. <p class="card-text"><strong>Keywords:</strong> <a href="https://publications.waset.org/abstracts/search?q=data%20acquisition%20system" title="data acquisition system">data acquisition system</a>, <a href="https://publications.waset.org/abstracts/search?q=design%20device" title=" design device"> design device</a>, <a href="https://publications.waset.org/abstracts/search?q=weather%20development" title=" weather development"> weather development</a>, <a href="https://publications.waset.org/abstracts/search?q=predict%20precipitation%20and%20%28FUTA%29%20standard%20device" title=" predict precipitation and (FUTA) standard device"> predict precipitation and (FUTA) standard device</a> </p> <a href="https://publications.waset.org/abstracts/149813/design-of-a-standard-weather-data-acquisition-device-for-the-federal-university-of-technology-akure-nigeria" class="btn btn-primary btn-sm">Procedia</a> <a href="https://publications.waset.org/abstracts/149813.pdf" target="_blank" class="btn btn-primary btn-sm">PDF</a> <span class="bg-info text-light px-1 py-1 float-right rounded"> Downloads <span class="badge badge-light">91</span> </span> </div> </div> <div class="card paper-listing mb-3 mt-3"> <h5 class="card-header" style="font-size:.9rem"><span class="badge badge-info">29</span> Estimation of Service Quality and Its Impact on Market Share Using Business Analytics</h5> <div class="card-body"> <p class="card-text"><strong>Authors:</strong> <a href="https://publications.waset.org/abstracts/search?q=Haritha%20Saranga">Haritha Saranga</a> </p> <p class="card-text"><strong>Abstract:</strong></p> Service quality has become an important driver of competition in manufacturing industries of late, as many products are being sold in conjunction with service offerings. With increase in computational power and data capture capabilities, it has become possible to analyze and estimate various aspects of service quality at the granular level and determine their impact on business performance. In the current study context, dealer level, model-wise warranty data from one of the top two-wheeler manufacturers in India is used to estimate service quality of individual dealers and its impact on warranty related costs and sales performance. We collected primary data on warranty costs, number of complaints, monthly sales, type of quality upgrades, etc. from the two-wheeler automaker. In addition, we gathered secondary data on various regions in India, such as petrol and diesel prices, geographic and climatic conditions of various regions where the dealers are located, to control for customer usage patterns. We analyze this primary and secondary data with the help of a variety of analytics tools such as Auto-Regressive Integrated Moving Average (ARIMA), Seasonal ARIMA and ARIMAX. Study results, after controlling for a variety of factors, such as size, age, region of the dealership, and customer usage pattern, show that service quality does influence sales of the products in a significant manner. A more nuanced analysis reveals the dynamics between product quality and service quality, and how their interaction affects sales performance in the Indian two-wheeler industry context. We also provide various managerial insights using descriptive analytics and build a model that can provide sales projections using a variety of forecasting techniques. <p class="card-text"><strong>Keywords:</strong> <a href="https://publications.waset.org/abstracts/search?q=service%20quality" title="service quality">service quality</a>, <a href="https://publications.waset.org/abstracts/search?q=product%20quality" title=" product quality"> product quality</a>, <a href="https://publications.waset.org/abstracts/search?q=automobile%20industry" title=" automobile industry"> automobile industry</a>, <a href="https://publications.waset.org/abstracts/search?q=business%20analytics" title=" business analytics"> business analytics</a>, <a href="https://publications.waset.org/abstracts/search?q=auto-regressive%20integrated%20moving%20average" title=" auto-regressive integrated moving average"> auto-regressive integrated moving average</a> </p> <a href="https://publications.waset.org/abstracts/99732/estimation-of-service-quality-and-its-impact-on-market-share-using-business-analytics" class="btn btn-primary btn-sm">Procedia</a> <a href="https://publications.waset.org/abstracts/99732.pdf" target="_blank" class="btn btn-primary btn-sm">PDF</a> <span class="bg-info text-light px-1 py-1 float-right rounded"> Downloads <span class="badge badge-light">120</span> </span> </div> </div> <div class="card paper-listing mb-3 mt-3"> <h5 class="card-header" style="font-size:.9rem"><span class="badge badge-info">28</span> Lee-Carter Mortality Forecasting Method with Dynamic Normal Inverse Gaussian Mortality Index </h5> <div class="card-body"> <p class="card-text"><strong>Authors:</strong> <a href="https://publications.waset.org/abstracts/search?q=Funda%20Kul">Funda Kul</a>, <a href="https://publications.waset.org/abstracts/search?q=%C4%B0smail%20G%C3%BCr"> İsmail Gür</a> </p> <p class="card-text"><strong>Abstract:</strong></p> Pension scheme providers have to price mortality risk by accurate mortality forecasting method. There are many mortality-forecasting methods constructed and used in literature. The Lee-Carter model is the first model to consider stochastic improvement trends in life expectancy. It is still precisely used. Mortality forecasting is done by mortality index in the Lee-Carter model. It is assumed that mortality index fits ARIMA time series model. In this paper, we propose and use dynamic normal inverse gaussian distribution to modeling mortality indes in the Lee-Carter model. Using population mortality data for Italy, France, and Turkey, the model is forecasting capability is investigated, and a comparative analysis with other models is ensured by some well-known benchmarking criterions. <p class="card-text"><strong>Keywords:</strong> <a href="https://publications.waset.org/abstracts/search?q=mortality" title="mortality">mortality</a>, <a href="https://publications.waset.org/abstracts/search?q=forecasting" title=" forecasting"> forecasting</a>, <a href="https://publications.waset.org/abstracts/search?q=lee-carter%20model" title=" lee-carter model"> lee-carter model</a>, <a href="https://publications.waset.org/abstracts/search?q=normal%20inverse%20gaussian%20distribution" title=" normal inverse gaussian distribution"> normal inverse gaussian distribution</a> </p> <a href="https://publications.waset.org/abstracts/39750/lee-carter-mortality-forecasting-method-with-dynamic-normal-inverse-gaussian-mortality-index" class="btn btn-primary btn-sm">Procedia</a> <a href="https://publications.waset.org/abstracts/39750.pdf" target="_blank" class="btn btn-primary btn-sm">PDF</a> <span class="bg-info text-light px-1 py-1 float-right rounded"> Downloads <span class="badge badge-light">360</span> </span> </div> </div> <div class="card paper-listing mb-3 mt-3"> <h5 class="card-header" style="font-size:.9rem"><span class="badge badge-info">27</span> Review on Rainfall Prediction Using Machine Learning Technique</h5> <div class="card-body"> <p class="card-text"><strong>Authors:</strong> <a href="https://publications.waset.org/abstracts/search?q=Prachi%20Desai">Prachi Desai</a>, <a href="https://publications.waset.org/abstracts/search?q=Ankita%20Gandhi"> Ankita Gandhi</a>, <a href="https://publications.waset.org/abstracts/search?q=Mitali%20Acharya"> Mitali Acharya</a> </p> <p class="card-text"><strong>Abstract:</strong></p> Rainfall forecast is mainly used for predictions of rainfall in a specified area and determining their future rainfall conditions. Rainfall is always a global issue as it affects all major aspects of one's life. Agricultural, fisheries, forestry, tourism industry and other industries are widely affected by these conditions. The studies have resulted in insufficient availability of water resources and an increase in water demand in the near future. We already have a new forecast system that uses the deep Convolutional Neural Network (CNN) to forecast monthly rainfall and climate changes. We have also compared CNN against Artificial Neural Networks (ANN). Machine Learning techniques that are used in rainfall predictions include ARIMA Model, ANN, LR, SVM etc. The dataset on which we are experimenting is gathered online over the year 1901 to 20118. Test results have suggested more realistic improvements than conventional rainfall forecasts. <p class="card-text"><strong>Keywords:</strong> <a href="https://publications.waset.org/abstracts/search?q=ANN" title="ANN">ANN</a>, <a href="https://publications.waset.org/abstracts/search?q=CNN" title=" CNN"> CNN</a>, <a href="https://publications.waset.org/abstracts/search?q=supervised%20learning" title=" supervised learning"> supervised learning</a>, <a href="https://publications.waset.org/abstracts/search?q=machine%20learning" title=" machine learning"> machine learning</a>, <a href="https://publications.waset.org/abstracts/search?q=deep%20learning" title=" deep learning"> deep learning</a> </p> <a href="https://publications.waset.org/abstracts/146605/review-on-rainfall-prediction-using-machine-learning-technique" class="btn btn-primary btn-sm">Procedia</a> <a href="https://publications.waset.org/abstracts/146605.pdf" target="_blank" class="btn btn-primary btn-sm">PDF</a> <span class="bg-info text-light px-1 py-1 float-right rounded"> Downloads <span class="badge badge-light">201</span> </span> </div> </div> <div class="card paper-listing mb-3 mt-3"> <h5 class="card-header" style="font-size:.9rem"><span class="badge badge-info">26</span> Stock Price Prediction Using Time Series Algorithms</h5> <div class="card-body"> <p class="card-text"><strong>Authors:</strong> <a href="https://publications.waset.org/abstracts/search?q=Sumit%20Sen">Sumit Sen</a>, <a href="https://publications.waset.org/abstracts/search?q=Sohan%20Khedekar"> Sohan Khedekar</a>, <a href="https://publications.waset.org/abstracts/search?q=Umang%20Shinde"> Umang Shinde</a>, <a href="https://publications.waset.org/abstracts/search?q=Shivam%20Bhargava"> Shivam Bhargava</a> </p> <p class="card-text"><strong>Abstract:</strong></p> This study has been undertaken to investigate whether the deep learning models are able to predict the future stock prices by training the model with the historical stock price data. Since this work required time series analysis, various models are present today to perform time series analysis such as Recurrent Neural Network LSTM, ARIMA and Facebook Prophet. Applying these models the movement of stock price of stocks are predicted and also tried to provide the future prediction of the stock price of a stock. Final product will be a stock price prediction web application that is developed for providing the user the ease of analysis of the stocks and will also provide the predicted stock price for the next seven days. <p class="card-text"><strong>Keywords:</strong> <a href="https://publications.waset.org/abstracts/search?q=Autoregressive%20Integrated%20Moving%20Average" title="Autoregressive Integrated Moving Average">Autoregressive Integrated Moving Average</a>, <a href="https://publications.waset.org/abstracts/search?q=Deep%20Learning" title=" Deep Learning"> Deep Learning</a>, <a href="https://publications.waset.org/abstracts/search?q=Long%20Short%20Term%20Memory" title=" Long Short Term Memory"> Long Short Term Memory</a>, <a href="https://publications.waset.org/abstracts/search?q=Time-series" title=" Time-series"> Time-series</a> </p> <a href="https://publications.waset.org/abstracts/137402/stock-price-prediction-using-time-series-algorithms" class="btn btn-primary btn-sm">Procedia</a> <a href="https://publications.waset.org/abstracts/137402.pdf" target="_blank" class="btn btn-primary btn-sm">PDF</a> <span class="bg-info text-light px-1 py-1 float-right rounded"> Downloads <span class="badge badge-light">141</span> </span> </div> </div> <div class="card paper-listing mb-3 mt-3"> <h5 class="card-header" style="font-size:.9rem"><span class="badge badge-info">25</span> Forecasting Issues in Energy Markets within a Reg-ARIMA Framework</h5> <div class="card-body"> <p class="card-text"><strong>Authors:</strong> <a href="https://publications.waset.org/abstracts/search?q=Ilaria%20Lucrezia%20Amerise">Ilaria Lucrezia Amerise</a> </p> <p class="card-text"><strong>Abstract:</strong></p> Electricity markets throughout the world have undergone substantial changes. Accurate, reliable, clear and comprehensible modeling and forecasting of different variables (loads and prices in the first instance) have achieved increasing importance. In this paper, we describe the actual state of the art focusing on reg-SARMA methods, which have proven to be flexible enough to accommodate the electricity price/load behavior satisfactory. More specifically, we will discuss: 1) The dichotomy between point and interval forecasts; 2) The difficult choice between stochastic (e.g. climatic variation) and non-deterministic predictors (e.g. calendar variables); 3) The confrontation between modelling a single aggregate time series or creating separated and potentially different models of sub-series. The noteworthy point that we would like to make it emerge is that prices and loads require different approaches that appear irreconcilable even though must be made reconcilable for the interests and activities of energy companies. <p class="card-text"><strong>Keywords:</strong> <a href="https://publications.waset.org/abstracts/search?q=interval%20forecasts" title="interval forecasts">interval forecasts</a>, <a href="https://publications.waset.org/abstracts/search?q=time%20series" title=" time series"> time series</a>, <a href="https://publications.waset.org/abstracts/search?q=electricity%20prices" title=" electricity prices"> electricity prices</a>, <a href="https://publications.waset.org/abstracts/search?q=reg-SARIMA%20methods" title=" reg-SARIMA methods"> reg-SARIMA methods</a> </p> <a href="https://publications.waset.org/abstracts/104049/forecasting-issues-in-energy-markets-within-a-reg-arima-framework" class="btn btn-primary btn-sm">Procedia</a> <a href="https://publications.waset.org/abstracts/104049.pdf" target="_blank" class="btn btn-primary btn-sm">PDF</a> <span class="bg-info text-light px-1 py-1 float-right rounded"> Downloads <span class="badge badge-light">131</span> </span> </div> </div> <div class="card paper-listing mb-3 mt-3"> <h5 class="card-header" style="font-size:.9rem"><span class="badge badge-info">24</span> Forecasting Exchange Rate between Thai Baht and the US Dollar Using Time Series Analysis</h5> <div class="card-body"> <p class="card-text"><strong>Authors:</strong> <a href="https://publications.waset.org/abstracts/search?q=Kunya%20Bowornchockchai">Kunya Bowornchockchai</a> </p> <p class="card-text"><strong>Abstract:</strong></p> The objective of this research is to forecast the monthly exchange rate between Thai baht and the US dollar and to compare two forecasting methods. The methods are Box-Jenkins’ method and Holt’s method. Results show that the Box-Jenkins’ method is the most suitable method for the monthly Exchange Rate between Thai Baht and the US Dollar. The suitable forecasting model is ARIMA (1,1,0)  without constant and the forecasting equation is <span style="line-height: 20.8px;">Y</span><sub style="line-height: 20.8px;">t</sub><em> = </em>Y<sub>t-1</sub> + 0.3691 (Y<sub>t-1</sub> - Y<sub>t-2</sub>) When Y<sub>t</sub>  is the time series data at time t, respectively. <p class="card-text"><strong>Keywords:</strong> <a href="https://publications.waset.org/abstracts/search?q=Box%E2%80%93Jenkins%20method" title="Box–Jenkins method">Box–Jenkins method</a>, <a href="https://publications.waset.org/abstracts/search?q=Holt%E2%80%99s%20method" title=" Holt’s method"> Holt’s method</a>, <a href="https://publications.waset.org/abstracts/search?q=mean%20absolute%20percentage%20error%20%28MAPE%29" title=" mean absolute percentage error (MAPE)"> mean absolute percentage error (MAPE)</a>, <a href="https://publications.waset.org/abstracts/search?q=exchange%20rate" title=" exchange rate"> exchange rate</a> </p> <a href="https://publications.waset.org/abstracts/10818/forecasting-exchange-rate-between-thai-baht-and-the-us-dollar-using-time-series-analysis" class="btn btn-primary btn-sm">Procedia</a> <a href="https://publications.waset.org/abstracts/10818.pdf" target="_blank" class="btn btn-primary btn-sm">PDF</a> <span class="bg-info text-light px-1 py-1 float-right rounded"> Downloads <span class="badge badge-light">254</span> </span> </div> </div> <div class="card paper-listing mb-3 mt-3"> <h5 class="card-header" style="font-size:.9rem"><span class="badge badge-info">23</span> Times Series Analysis of Depositing in Industrial Design in Brazil between 1996 and 2013</h5> <div class="card-body"> <p class="card-text"><strong>Authors:</strong> <a href="https://publications.waset.org/abstracts/search?q=Jonas%20Pedro%20Fabris">Jonas Pedro Fabris</a>, <a href="https://publications.waset.org/abstracts/search?q=Alberth%20Almeida%20Amorim%20Souza"> Alberth Almeida Amorim Souza</a>, <a href="https://publications.waset.org/abstracts/search?q=Maria%20Emilia%20Camargo"> Maria Emilia Camargo</a>, <a href="https://publications.waset.org/abstracts/search?q=Suzana%20Leit%C3%A3o%20Russo"> Suzana Leitão Russo</a> </p> <p class="card-text"><strong>Abstract:</strong></p> With the law Nº. 9279, of May 14, 1996, the Brazilian government regulates rights and obligations relating to industrial property considering the economic development of the country as granting patents, trademark registration, registration of industrial designs and other forms of protection copyright. In this study, we show the application of the methodology of Box and Jenkins in the series of deposits of industrial design at the National Institute of Industrial Property for the period from May 1996 to April 2013. First, a graphical analysis of the data was done by observing the behavior of the data and the autocorrelation function. The best model found, based on the analysis of charts and statistical tests suggested by Box and Jenkins methodology, it was possible to determine the model number for the deposit of industrial design, SARIMA (2,1,0)(2,0,0), with an equal to 9.88% MAPE. <p class="card-text"><strong>Keywords:</strong> <a href="https://publications.waset.org/abstracts/search?q=ARIMA%20models" title="ARIMA models">ARIMA models</a>, <a href="https://publications.waset.org/abstracts/search?q=autocorrelation" title=" autocorrelation"> autocorrelation</a>, <a href="https://publications.waset.org/abstracts/search?q=Box%20and%20Jenkins%20Models" title=" Box and Jenkins Models"> Box and Jenkins Models</a>, <a href="https://publications.waset.org/abstracts/search?q=industrial%20design" title=" industrial design"> industrial design</a>, <a href="https://publications.waset.org/abstracts/search?q=MAPE" title=" MAPE"> MAPE</a>, <a href="https://publications.waset.org/abstracts/search?q=time%20series" title=" time series"> time series</a> </p> <a href="https://publications.waset.org/abstracts/26148/times-series-analysis-of-depositing-in-industrial-design-in-brazil-between-1996-and-2013" class="btn btn-primary btn-sm">Procedia</a> <a href="https://publications.waset.org/abstracts/26148.pdf" target="_blank" class="btn btn-primary btn-sm">PDF</a> <span class="bg-info text-light px-1 py-1 float-right rounded"> Downloads <span class="badge badge-light">544</span> </span> </div> </div> <div class="card paper-listing mb-3 mt-3"> <h5 class="card-header" style="font-size:.9rem"><span class="badge badge-info">22</span> Energy Communities from Municipality Level to Province Level: A Comparison Using Autoregressive Integrated Moving Average Model</h5> <div class="card-body"> <p class="card-text"><strong>Authors:</strong> <a href="https://publications.waset.org/abstracts/search?q=Amro%20Issam%20Hamed%20Attia%20Ramadan">Amro Issam Hamed Attia Ramadan</a>, <a href="https://publications.waset.org/abstracts/search?q=Marco%20Zappatore"> Marco Zappatore</a>, <a href="https://publications.waset.org/abstracts/search?q=Pasquale%20Balena"> Pasquale Balena</a>, <a href="https://publications.waset.org/abstracts/search?q=Antonella%20Longo"> Antonella Longo</a> </p> <p class="card-text"><strong>Abstract:</strong></p> Considering the energetic crisis that is hitting Europe, it becomes more and more necessary to change the energy policies to depend less on fossil fuels and replace them with energy from renewable sources. This has triggered the urge to use clean energy not only to satisfy energy needs and fulfill the required consumption but also to decrease the danger of climatic changes due to harmful emissions. Many countries have already started creating energetic communities based on renewable energy sources. The first step to understanding energy needs in any place is to perfectly know the consumption. In this work, we aim to estimate electricity consumption for a municipality that makes up part of a rural area located in southern Italy using forecast models that allow for the estimation of electricity consumption for the next ten years, and we then apply the same model to the province where the municipality is located and estimate the future consumption for the same period to examine whether it is possible to start from the municipality level to reach the province level when creating energy communities. <p class="card-text"><strong>Keywords:</strong> <a href="https://publications.waset.org/abstracts/search?q=ARIMA" title="ARIMA">ARIMA</a>, <a href="https://publications.waset.org/abstracts/search?q=electricity%20consumption" title=" electricity consumption"> electricity consumption</a>, <a href="https://publications.waset.org/abstracts/search?q=forecasting%20models" title=" forecasting models"> forecasting models</a>, <a href="https://publications.waset.org/abstracts/search?q=time%20series" title=" time series"> time series</a> </p> <a href="https://publications.waset.org/abstracts/159126/energy-communities-from-municipality-level-to-province-level-a-comparison-using-autoregressive-integrated-moving-average-model" class="btn btn-primary btn-sm">Procedia</a> <a href="https://publications.waset.org/abstracts/159126.pdf" target="_blank" class="btn btn-primary btn-sm">PDF</a> <span class="bg-info text-light px-1 py-1 float-right rounded"> Downloads <span class="badge badge-light">174</span> </span> </div> </div> <div class="card paper-listing mb-3 mt-3"> <h5 class="card-header" style="font-size:.9rem"><span class="badge badge-info">21</span> Automated Machine Learning Algorithm Using Recurrent Neural Network to Perform Long-Term Time Series Forecasting</h5> <div class="card-body"> <p class="card-text"><strong>Authors:</strong> <a href="https://publications.waset.org/abstracts/search?q=Ying%20Su">Ying Su</a>, <a href="https://publications.waset.org/abstracts/search?q=Morgan%20C.%20Wang"> Morgan C. Wang</a> </p> <p class="card-text"><strong>Abstract:</strong></p> Long-term time series forecasting is an important research area for automated machine learning (AutoML). Currently, forecasting based on either machine learning or statistical learning is usually built by experts, and it requires significant manual effort, from model construction, feature engineering, and hyper-parameter tuning to the construction of the time series model. Automation is not possible since there are too many human interventions. To overcome these limitations, this article proposed to use recurrent neural networks (RNN) through the memory state of RNN to perform long-term time series prediction. We have shown that this proposed approach is better than the traditional Autoregressive Integrated Moving Average (ARIMA). In addition, we also found it is better than other network systems, including Fully Connected Neural Networks (FNN), Convolutional Neural Networks (CNN), and Nonpooling Convolutional Neural Networks (NPCNN). <p class="card-text"><strong>Keywords:</strong> <a href="https://publications.waset.org/abstracts/search?q=automated%20machines%20learning" title="automated machines learning">automated machines learning</a>, <a href="https://publications.waset.org/abstracts/search?q=autoregressive%20integrated%20moving%20average" title=" autoregressive integrated moving average"> autoregressive integrated moving average</a>, <a href="https://publications.waset.org/abstracts/search?q=neural%20networks" title=" neural networks"> neural networks</a>, <a href="https://publications.waset.org/abstracts/search?q=time%20series%20analysis" title=" time series analysis"> time series analysis</a> </p> <a href="https://publications.waset.org/abstracts/173817/automated-machine-learning-algorithm-using-recurrent-neural-network-to-perform-long-term-time-series-forecasting" class="btn btn-primary btn-sm">Procedia</a> <a href="https://publications.waset.org/abstracts/173817.pdf" target="_blank" class="btn btn-primary btn-sm">PDF</a> <span class="bg-info text-light px-1 py-1 float-right rounded"> Downloads <span class="badge badge-light">105</span> </span> </div> </div> <ul class="pagination"> <li class="page-item disabled"><span class="page-link">&lsaquo;</span></li> <li class="page-item active"><span class="page-link">1</span></li> <li class="page-item"><a class="page-link" href="https://publications.waset.org/abstracts/search?q=ARIMA&page=2">2</a></li> <li class="page-item"><a class="page-link" href="https://publications.waset.org/abstracts/search?q=ARIMA&page=2" rel="next">&rsaquo;</a></li> </ul> </div> </main> <footer> <div id="infolinks" class="pt-3 pb-2"> <div class="container"> <div 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