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auto.arima</title> <meta name="description" content="Search results for: auto.arima"> <meta name="keywords" content="auto.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 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id="custom-search-input"> <div class="input-group"> <i class="fas fa-search"></i> <input type="text" class="search-query" name="q" placeholder="Author, Title, Abstract, Keywords" value="auto.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> 372</div> </div> </div> </div> <h1 class="mt-3 mb-3 text-center" style="font-size:1.6rem;">Search results for: auto.arima</h1> <div class="card paper-listing mb-3 mt-3"> <h5 class="card-header" style="font-size:.9rem"><span class="badge badge-info">372</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">371</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">419</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">370</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">369</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">368</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">367</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">366</span> A Method of the Semantic on Image Auto-Annotation</h5> <div class="card-body"> <p class="card-text"><strong>Authors:</strong> <a href="https://publications.waset.org/abstracts/search?q=Lin%20Huo">Lin Huo</a>, <a href="https://publications.waset.org/abstracts/search?q=Xianwei%20Liu"> Xianwei Liu</a>, <a href="https://publications.waset.org/abstracts/search?q=Jingxiong%20Zhou"> Jingxiong Zhou</a> </p> <p class="card-text"><strong>Abstract:</strong></p> Recently, due to the existence of semantic gap between image visual features and human concepts, the semantic of image auto-annotation has become an important topic. Firstly, by extract low-level visual features of the image, and the corresponding Hash method, mapping the feature into the corresponding Hash coding, eventually, transformed that into a group of binary string and store it, image auto-annotation by search is a popular method, we can use it to design and implement a method of image semantic auto-annotation. Finally, Through the test based on the Corel image set, and the results show that, this method is effective. <p class="card-text"><strong>Keywords:</strong> <a href="https://publications.waset.org/abstracts/search?q=image%20auto-annotation" title="image auto-annotation">image auto-annotation</a>, <a href="https://publications.waset.org/abstracts/search?q=color%20correlograms" title=" color correlograms"> color correlograms</a>, <a href="https://publications.waset.org/abstracts/search?q=Hash%20code" title=" Hash code"> Hash code</a>, <a href="https://publications.waset.org/abstracts/search?q=image%20retrieval" title=" image retrieval"> image retrieval</a> </p> <a href="https://publications.waset.org/abstracts/15628/a-method-of-the-semantic-on-image-auto-annotation" class="btn btn-primary btn-sm">Procedia</a> <a href="https://publications.waset.org/abstracts/15628.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">497</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">365</span> Design of an Energy Efficient Electric Auto Rickshaw</h5> <div class="card-body"> <p class="card-text"><strong>Authors:</strong> <a href="https://publications.waset.org/abstracts/search?q=Muhammad%20Asghar">Muhammad Asghar</a>, <a href="https://publications.waset.org/abstracts/search?q=Aamer%20Iqbal%20Bhatti"> Aamer Iqbal Bhatti</a>, <a href="https://publications.waset.org/abstracts/search?q=Qadeer%20Ahmed"> Qadeer Ahmed</a>, <a href="https://publications.waset.org/abstracts/search?q=Tahir%20Izhar"> Tahir Izhar</a> </p> <p class="card-text"><strong>Abstract:</strong></p> Three wheeler auto Rickshaw, often termed as ‘auto rickshaw’ is very common in Pakistan and is considered as the most affordable means of transportation to the local people. Problems caused by the gasoline engine on the environment and people, the researchers and the automotive industry have turned to the hybrid electric vehicles and electrical powered vehicle. The research in this paper explains the design of energy efficient Electric auto Rickshaw. An electric auto rickshaw is being developed at Center for Energy Research and Development, (Lahore), which is running on the roads of Lahore city. Energy storage capacity of batteries is at least 25 times heavier than fossil fuel and having volume 10 times in comparison to fuel, resulting an increase of the Rickshaw weight. A set of specifications is derived according to the mobility requirements of the electric auto rickshaw. The design choices considering the power-train and component selection are explained in detail. It was concluded that electric auto rickshaw has many advantages and benefits over the conventional auto rickshaw. It is cleaner and much more energy efficient but limited to the distance it can travel before recharging of battery. In addition, a brief future view of the battery technology is given. <p class="card-text"><strong>Keywords:</strong> <a href="https://publications.waset.org/abstracts/search?q=conventional%20auto%20rickshaw" title="conventional auto rickshaw">conventional auto rickshaw</a>, <a href="https://publications.waset.org/abstracts/search?q=energy%20efficiency" title=" energy efficiency"> energy efficiency</a>, <a href="https://publications.waset.org/abstracts/search?q=electric%20auto%20rickshaw" title=" electric auto rickshaw"> electric auto rickshaw</a>, <a href="https://publications.waset.org/abstracts/search?q=internal%20combustion%20engine" title=" internal combustion engine"> internal combustion engine</a>, <a href="https://publications.waset.org/abstracts/search?q=environment" title=" environment"> environment</a> </p> <a href="https://publications.waset.org/abstracts/54016/design-of-an-energy-efficient-electric-auto-rickshaw" class="btn btn-primary btn-sm">Procedia</a> <a href="https://publications.waset.org/abstracts/54016.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">364</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">363</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">362</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">361</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">360</span> GPS Devices to Increase Efficiency of Indian Auto-Rickshaw Segment</h5> <div class="card-body"> <p class="card-text"><strong>Authors:</strong> <a href="https://publications.waset.org/abstracts/search?q=Sanchay%20Vaidya">Sanchay Vaidya</a>, <a href="https://publications.waset.org/abstracts/search?q=Sourabh%20Gupta"> Sourabh Gupta</a>, <a href="https://publications.waset.org/abstracts/search?q=Gouresh%20Singhal"> Gouresh Singhal</a> </p> <p class="card-text"><strong>Abstract:</strong></p> There are various modes of transport in metro cities in India, auto-rickshaws being one of them. Auto-rickshaws provide connectivity to all the places in the city offering last mile connectivity. Among all the modes of transport, the auto-rickshaw industry is the most unorganized and inefficient. Although unions exist in different cities they aren’t good enough to cope up with the upcoming advancements in the field of technology. An introduction of simple technology in this field may do wonder and help increase the revenues. This paper aims to organize this segment under a single umbrella using GPS devices and mobile phones. The paper includes surveys of about 300 auto-rickshaw drivers and 1000 plus commuters across 6 metro cities in India. Carrying out research and analysis provides a base for the development of this model and implementation of this innovative technique, which is discussed in this paper in detail with ample emphasis given on the implementation of this model. <p class="card-text"><strong>Keywords:</strong> <a href="https://publications.waset.org/abstracts/search?q=auto-rickshaws" title="auto-rickshaws">auto-rickshaws</a>, <a href="https://publications.waset.org/abstracts/search?q=business%20model" title=" business model"> business model</a>, <a href="https://publications.waset.org/abstracts/search?q=GPS%20device" title=" GPS device"> GPS device</a>, <a href="https://publications.waset.org/abstracts/search?q=mobile%20application" title=" mobile application"> mobile application</a> </p> <a href="https://publications.waset.org/abstracts/6574/gps-devices-to-increase-efficiency-of-indian-auto-rickshaw-segment" class="btn btn-primary btn-sm">Procedia</a> <a href="https://publications.waset.org/abstracts/6574.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">227</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">359</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">358</span> Parking Space Detection and Trajectory Tracking Control for Vehicle Auto-Parking</h5> <div class="card-body"> <p class="card-text"><strong>Authors:</strong> <a href="https://publications.waset.org/abstracts/search?q=Shiuh-Jer%20Huang">Shiuh-Jer Huang</a>, <a href="https://publications.waset.org/abstracts/search?q=Yu-Sheng%20Hsu"> Yu-Sheng Hsu</a> </p> <p class="card-text"><strong>Abstract:</strong></p> On-board available parking space detecting system, parking trajectory planning and tracking control mechanism are the key components of vehicle backward auto-parking system. Firstly, pair of ultrasonic sensors is installed on each side of vehicle body surface to detect the relative distance between ego-car and surrounding obstacle. The dimension of a found empty space can be calculated based on vehicle speed and the time history of ultrasonic sensor detecting information. This result can be used for constructing the 2D vehicle environmental map and available parking type judgment. Finally, the auto-parking controller executes the on-line optimal parking trajectory planning based on this 2D environmental map, and monitors the real-time vehicle parking trajectory tracking control. This low cost auto-parking system was tested on a model car. <p class="card-text"><strong>Keywords:</strong> <a href="https://publications.waset.org/abstracts/search?q=vehicle%20auto-parking" title="vehicle auto-parking">vehicle auto-parking</a>, <a href="https://publications.waset.org/abstracts/search?q=parking%20space%20detection" title=" parking space detection"> parking space detection</a>, <a href="https://publications.waset.org/abstracts/search?q=parking%20path%20tracking%20control" title=" parking path tracking control"> parking path tracking control</a>, <a href="https://publications.waset.org/abstracts/search?q=intelligent%20fuzzy%20controller" title=" intelligent fuzzy controller"> intelligent fuzzy controller</a> </p> <a href="https://publications.waset.org/abstracts/78571/parking-space-detection-and-trajectory-tracking-control-for-vehicle-auto-parking" class="btn btn-primary btn-sm">Procedia</a> <a href="https://publications.waset.org/abstracts/78571.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">357</span> Production of Sr-Ferrite Sub-Micron Powder by Conventional and Sol-Gel Auto-Combustion Methods </h5> <div class="card-body"> <p class="card-text"><strong>Authors:</strong> <a href="https://publications.waset.org/abstracts/search?q=M.%20Ghobeiti-Hasab">M. Ghobeiti-Hasab</a> </p> <p class="card-text"><strong>Abstract:</strong></p> Magnetic powder of Sr-ferrite was prepared by conventional and sol-gel auto-combustion methods. In conventional method, strontium carbonate and ferric oxide powders were mixed together and then mixture was calcined. In sol-gel auto-combustion method, a solution containing strontium nitrate, ferric nitrate and citric acid was heated until the combustion took place automatically; then, as-burnt powder was calcined. Thermal behavior, phase identification, morphology and magnetic properties of powders obtained by these two methods were compared by DTA, XRD, SEM, and VSM techniques. According to the results of DTA analysis, formation temperature of Sr-ferrite obtained by conventional and sol-gel auto-combustion methods were 1300 °C and 1000 °C, respectively. XRD results confirmed the formation of pure Sr-ferrite at the mentioned temperatures. Plate and hexagonal-shape particles of Sr-ferrite were observed using SEM. The Sr-ferrite powder obtained by sol-gel auto-combustion method had saturation magnetization of 66.03 emu/g and coercivity of 5731 Oe in comparison with values of 58.20 emu/g and 4378 Oe obtained by conventional method. <p class="card-text"><strong>Keywords:</strong> <a href="https://publications.waset.org/abstracts/search?q=Sr-ferrite" title="Sr-ferrite">Sr-ferrite</a>, <a href="https://publications.waset.org/abstracts/search?q=sol-gel" title=" sol-gel"> sol-gel</a>, <a href="https://publications.waset.org/abstracts/search?q=magnetic%20properties" title=" magnetic properties"> magnetic properties</a>, <a href="https://publications.waset.org/abstracts/search?q=calcination" title=" calcination"> calcination</a> </p> <a href="https://publications.waset.org/abstracts/19970/production-of-sr-ferrite-sub-micron-powder-by-conventional-and-sol-gel-auto-combustion-methods" class="btn btn-primary btn-sm">Procedia</a> <a href="https://publications.waset.org/abstracts/19970.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">238</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">356</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">355</span> Customers' Perception towards the Service Marketing Mix and Frequency of Use of Mercedes Benz Automobile Service, Thailand</h5> <div class="card-body"> <p class="card-text"><strong>Authors:</strong> <a href="https://publications.waset.org/abstracts/search?q=Pranee%20Tridhoskul">Pranee Tridhoskul</a> </p> <p class="card-text"><strong>Abstract:</strong></p> This research paper is aimed to examine a relationship between the service marketing mix and customers’ frequency of use of service at Mercedes Benz Auto Repair Centres under Thonburi Group, Thailand. Based on 2,267 customers who used the service of Thonburi Group’s Auto Repair Centres as the population, the sampling of this research was a total of 340 samples, by use of Probability Sampling Technique. Systematic Random Sampling was applied by use of questionnaire in collecting the data at Thonburi Group’s Auto Repair Centres. Mean and Pearson’s basic statistical correlations were utilized in analyzing the data. The study discovered a medium level of customers’ perception towards product and service of Thonburi Group’s Auto Repair Centres, price, place or distribution channel and promotion. People who provided service were perceived also at a medium level, whereas the physical evidence and service process were perceived at a high level. Furthermore, there appeared a correlation between the physical evidence and service process, and customers’ frequency of use of automobile service per year. <p class="card-text"><strong>Keywords:</strong> <a href="https://publications.waset.org/abstracts/search?q=service%20marketing%20mix" title="service marketing mix">service marketing mix</a>, <a href="https://publications.waset.org/abstracts/search?q=behavior" title=" behavior"> behavior</a>, <a href="https://publications.waset.org/abstracts/search?q=Mercedes%20Auto%20Service%20Centre" title=" Mercedes Auto Service Centre"> Mercedes Auto Service Centre</a>, <a href="https://publications.waset.org/abstracts/search?q=frequency%20of%20use" title=" frequency of use"> frequency of use</a> </p> <a href="https://publications.waset.org/abstracts/8130/customers-perception-towards-the-service-marketing-mix-and-frequency-of-use-of-mercedes-benz-automobile-service-thailand" class="btn btn-primary btn-sm">Procedia</a> <a href="https://publications.waset.org/abstracts/8130.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">326</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">354</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">353</span> Auto-Tuning of CNC Parameters According to the Machining Mode Selection</h5> <div class="card-body"> <p class="card-text"><strong>Authors:</strong> <a href="https://publications.waset.org/abstracts/search?q=Jenq-Shyong%20Chen">Jenq-Shyong Chen</a>, <a href="https://publications.waset.org/abstracts/search?q=Ben-Fong%20Yu"> Ben-Fong Yu</a> </p> <p class="card-text"><strong>Abstract:</strong></p> CNC(computer numerical control) machining centers have been widely used for machining different metal components for various industries. For a specific CNC machine, its everyday job is assigned to cut different products with quite different attributes such as material type, workpiece weight, geometry, tooling, and cutting conditions. Theoretically, the dynamic characteristics of the CNC machine should be properly tuned match each machining job in order to get the optimal machining performance. However, most of the CNC machines are set with only a standard set of CNC parameters. In this study, we have developed an auto-tuning system which can automatically change the CNC parameters and in hence change the machine dynamic characteristics according to the selection of machining modes which are set by the mixed combination of three machine performance indexes: the HO (high surface quality) index, HP (high precision) index and HS (high speed) index. The acceleration, jerk, corner error tolerance, oscillation and dynamic bandwidth of machine’s feed axes have been changed according to the selection of the machine performance indexes. The proposed auto-tuning system of the CNC parameters has been implemented on a PC-based CNC controller and a three-axis machining center. The measured experimental result have shown the promising of our proposed auto-tuning system. <p class="card-text"><strong>Keywords:</strong> <a href="https://publications.waset.org/abstracts/search?q=auto-tuning" title="auto-tuning">auto-tuning</a>, <a href="https://publications.waset.org/abstracts/search?q=CNC%20parameters" title=" CNC parameters"> CNC parameters</a>, <a href="https://publications.waset.org/abstracts/search?q=machining%20mode" title=" machining mode"> machining mode</a>, <a href="https://publications.waset.org/abstracts/search?q=high%20speed" title=" high speed"> high speed</a>, <a href="https://publications.waset.org/abstracts/search?q=high%20accuracy" title=" high accuracy"> high accuracy</a>, <a href="https://publications.waset.org/abstracts/search?q=high%20surface%20quality" title=" high surface quality"> high surface quality</a> </p> <a href="https://publications.waset.org/abstracts/26213/auto-tuning-of-cnc-parameters-according-to-the-machining-mode-selection" class="btn btn-primary btn-sm">Procedia</a> <a href="https://publications.waset.org/abstracts/26213.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">380</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">352</span> Self-Tuning Robot Control Based on Subspace Identification</h5> <div class="card-body"> <p class="card-text"><strong>Authors:</strong> <a href="https://publications.waset.org/abstracts/search?q=Mathias%20Marquardt">Mathias Marquardt</a>, <a href="https://publications.waset.org/abstracts/search?q=Peter%20D%C3%BCnow"> Peter Dünow</a>, <a href="https://publications.waset.org/abstracts/search?q=Sandra%20Ba%C3%9Fler"> Sandra Baßler</a> </p> <p class="card-text"><strong>Abstract:</strong></p> The paper describes the use of subspace based identification methods for auto tuning of a state space control system. The plant is an unstable but self balancing transport robot. Because of the unstable character of the process it has to be identified from closed loop input-output data. Based on the identified model a state space controller combined with an observer is calculated. The subspace identification algorithm and the controller design procedure is combined to a auto tuning method. The capability of the approach was verified in a simulation experiments under different process conditions. <p class="card-text"><strong>Keywords:</strong> <a href="https://publications.waset.org/abstracts/search?q=auto%20tuning" title="auto tuning">auto tuning</a>, <a href="https://publications.waset.org/abstracts/search?q=balanced%20robot" title=" balanced robot"> balanced robot</a>, <a href="https://publications.waset.org/abstracts/search?q=closed%20loop%20identification" title=" closed loop identification"> closed loop identification</a>, <a href="https://publications.waset.org/abstracts/search?q=subspace%20identification" title=" subspace identification"> subspace identification</a> </p> <a href="https://publications.waset.org/abstracts/49108/self-tuning-robot-control-based-on-subspace-identification" class="btn btn-primary btn-sm">Procedia</a> <a href="https://publications.waset.org/abstracts/49108.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">380</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">351</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">350</span> Magnetic Properties of Sr-Ferrite Nano-Powder Synthesized by Sol-Gel Auto-Combustion Method </h5> <div class="card-body"> <p class="card-text"><strong>Authors:</strong> <a href="https://publications.waset.org/abstracts/search?q=M.%20Ghobeiti-Hasab">M. Ghobeiti-Hasab</a>, <a href="https://publications.waset.org/abstracts/search?q=Z.%20Shariati"> Z. Shariati</a> </p> <p class="card-text"><strong>Abstract:</strong></p> In this paper, strontium ferrite (SrO.6Fe2O3) was synthesized by the sol-gel auto-combustion process. The thermal behavior of powder obtained from self-propagating combustion of initial gel was evaluated by simultaneous differential thermal analysis (DTA) and thermo gravimetric (TG), from room temperature to 1200°C. The as-burnt powder was calcined at various temperatures from 700-900°C to achieve the single-phase Sr-ferrite. Phase composition, morphology and magnetic properties were investigated using X-ray diffraction (XRD), transmission electron microscopy (TEM) and vibrating sample magnetometry (VSM) techniques. Results showed that the single-phase and nano-sized hexagonal strontium ferrite particles were formed at calcination temperature of 800°C with crystallite size of 27 nm and coercivity of 6238 Oe. <p class="card-text"><strong>Keywords:</strong> <a href="https://publications.waset.org/abstracts/search?q=hard%20magnet" title="hard magnet">hard magnet</a>, <a href="https://publications.waset.org/abstracts/search?q=Sr-ferrite" title=" Sr-ferrite"> Sr-ferrite</a>, <a href="https://publications.waset.org/abstracts/search?q=sol-gel%20auto-combustion" title=" sol-gel auto-combustion"> sol-gel auto-combustion</a>, <a href="https://publications.waset.org/abstracts/search?q=nano-powder" title=" nano-powder"> nano-powder</a> </p> <a href="https://publications.waset.org/abstracts/13710/magnetic-properties-of-sr-ferrite-nano-powder-synthesized-by-sol-gel-auto-combustion-method" class="btn btn-primary btn-sm">Procedia</a> <a href="https://publications.waset.org/abstracts/13710.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">365</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">349</span> Design and Simulation on Technology Capabilities in Developing countries, Design and Engineering Approach</h5> <div class="card-body"> <p class="card-text"><strong>Authors:</strong> <a href="https://publications.waset.org/abstracts/search?q=S.%20Abedi">S. Abedi</a>, <a href="https://publications.waset.org/abstracts/search?q=M.%20R.%20Soroush"> M. R. Soroush</a>, <a href="https://publications.waset.org/abstracts/search?q=M.%20Mousakhani"> M. Mousakhani </a> </p> <p class="card-text"><strong>Abstract:</strong></p> According to studies in the field of technology capabilities we identify the most important indicators to evaluate the level of "Design and Engineering" capabilities. Since the technology development correlates with the level of technology capabilities trying to promote its key importance. In this research by using FDM, the right combination of D&E capabilities indicators according to the auto industry is presented. Finally, with modeling evaluation of D&E capabilities by using FIS and check its reliability, five levels were determined to evaluate the D&E capabilities. We have analyzed 80 companies in auto industry and determined D&E capabilities of each level. Field of company activity indicators has been divided into four categories, Suspension group, Electrical group, Engine groups and trims group. The results show that half of the surveyed companies had D&E capabilities in Level 1 and 2 or in other words very low and low level of D&E. <p class="card-text"><strong>Keywords:</strong> <a href="https://publications.waset.org/abstracts/search?q=developing%20countries" title="developing countries">developing countries</a>, <a href="https://publications.waset.org/abstracts/search?q=D%26E%20capabilities" title=" D&E capabilities"> D&E capabilities</a>, <a href="https://publications.waset.org/abstracts/search?q=technology%20capabilities" title=" technology capabilities"> technology capabilities</a>, <a href="https://publications.waset.org/abstracts/search?q=auto%20industry" title=" auto industry"> auto industry</a> </p> <a href="https://publications.waset.org/abstracts/26002/design-and-simulation-on-technology-capabilities-in-developing-countries-design-and-engineering-approach" class="btn btn-primary btn-sm">Procedia</a> <a href="https://publications.waset.org/abstracts/26002.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">535</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">348</span> Employment Opportunities in Automobile Sector-Indian Scenario</h5> <div class="card-body"> <p class="card-text"><strong>Authors:</strong> <a href="https://publications.waset.org/abstracts/search?q=A.%20K.%20Sarathe">A. K. Sarathe</a>, <a href="https://publications.waset.org/abstracts/search?q=N.%20P.%20Patidar"> N. P. Patidar</a> </p> <p class="card-text"><strong>Abstract:</strong></p> The Indian automobile sector is comprised of independent manufacturers and joint ventures with their foreign counterpart companies by making use of the Foreign Direct Investment policy of the Government of India. These manufacturers started capturing the hearts of Indian customers with their choice of technological and innovative product features, with quality and reliability. This transformed the automobile scene from a “sellers market to buyers market”. The potential benefits from the auto sector have been recognized by the planners, managers, and administrators of both the sectors –government and private. Generation of employment for the readily available technical workforce has been achieved not only through the manufacturers, but also through the growing ancillaries and service providers of the auto industries. The main purpose of this paper is to come up with the identification of possible working areas and associated job functions of mechanical and automobile diploma holders having employment opportunities in auto sector of India. <p class="card-text"><strong>Keywords:</strong> <a href="https://publications.waset.org/abstracts/search?q=automobile%20sector" title="automobile sector">automobile sector</a>, <a href="https://publications.waset.org/abstracts/search?q=diploma%20holder" title=" diploma holder"> diploma holder</a>, <a href="https://publications.waset.org/abstracts/search?q=employment" title=" employment"> employment</a>, <a href="https://publications.waset.org/abstracts/search?q=job%20description" title=" job description"> job description</a> </p> <a href="https://publications.waset.org/abstracts/16852/employment-opportunities-in-automobile-sector-indian-scenario" class="btn btn-primary btn-sm">Procedia</a> <a href="https://publications.waset.org/abstracts/16852.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">422</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">347</span> Unsupervised Feature Learning by Pre-Route Simulation of Auto-Encoder Behavior Model</h5> <div class="card-body"> <p class="card-text"><strong>Authors:</strong> <a href="https://publications.waset.org/abstracts/search?q=Youngjae%20Jin">Youngjae Jin</a>, <a href="https://publications.waset.org/abstracts/search?q=Daeshik%20Kim"> Daeshik Kim</a> </p> <p class="card-text"><strong>Abstract:</strong></p> This paper describes a cycle accurate simulation results of weight values learned by an auto-encoder behavior model in terms of pre-route simulation. Given the results we visualized the first layer representations with natural images. Many common deep learning threads have focused on learning high-level abstraction of unlabeled raw data by unsupervised feature learning. However, in the process of handling such a huge amount of data, the learning method’s computation complexity and time limited advanced research. These limitations came from the fact these algorithms were computed by using only single core CPUs. For this reason, parallel-based hardware, FPGAs, was seen as a possible solution to overcome these limitations. We adopted and simulated the ready-made auto-encoder to design a behavior model in Verilog HDL before designing hardware. With the auto-encoder behavior model pre-route simulation, we obtained the cycle accurate results of the parameter of each hidden layer by using MODELSIM. The cycle accurate results are very important factor in designing a parallel-based digital hardware. Finally this paper shows an appropriate operation of behavior model based pre-route simulation. Moreover, we visualized learning latent representations of the first hidden layer with Kyoto natural image dataset. <p class="card-text"><strong>Keywords:</strong> <a href="https://publications.waset.org/abstracts/search?q=auto-encoder" title="auto-encoder">auto-encoder</a>, <a href="https://publications.waset.org/abstracts/search?q=behavior%20model%20simulation" title=" behavior model simulation"> behavior model simulation</a>, <a href="https://publications.waset.org/abstracts/search?q=digital%20hardware%20design" title=" digital hardware design"> digital hardware design</a>, <a href="https://publications.waset.org/abstracts/search?q=pre-route%20simulation" title=" pre-route simulation"> pre-route simulation</a>, <a href="https://publications.waset.org/abstracts/search?q=Unsupervised%20feature%20learning" title=" Unsupervised feature learning"> Unsupervised feature learning</a> </p> <a href="https://publications.waset.org/abstracts/4980/unsupervised-feature-learning-by-pre-route-simulation-of-auto-encoder-behavior-model" class="btn btn-primary btn-sm">Procedia</a> <a href="https://publications.waset.org/abstracts/4980.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">446</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">346</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">345</span> Obtaining of Nanocrystalline Ferrites and Other Complex Oxides by Sol-Gel Method with Participation of Auto-Combustion</h5> <div class="card-body"> <p class="card-text"><strong>Authors:</strong> <a href="https://publications.waset.org/abstracts/search?q=V.%20S.%20Bushkova">V. S. Bushkova </a> </p> <p class="card-text"><strong>Abstract:</strong></p> It is well known that in recent years magnetic materials have received increased attention due to their properties. For this reason a significant number of patents that were published during the last decade are oriented towards synthesis and study of such materials. The aim of this work is to create and study ferrite nanocrystalline materials with spinel structure, using sol-gel technology with participation of auto-combustion. This method is perspective in that it is a cheap and low-temperature technique that allows for the fine control on the product’s chemical composition. <p class="card-text"><strong>Keywords:</strong> <a href="https://publications.waset.org/abstracts/search?q=magnetic%20materials" title="magnetic materials">magnetic materials</a>, <a href="https://publications.waset.org/abstracts/search?q=ferrites" title=" ferrites"> ferrites</a>, <a href="https://publications.waset.org/abstracts/search?q=sol-gel%20technology" title=" sol-gel technology"> sol-gel technology</a>, <a href="https://publications.waset.org/abstracts/search?q=nanocrystalline%20powders" title=" nanocrystalline powders"> nanocrystalline powders</a> </p> <a href="https://publications.waset.org/abstracts/19687/obtaining-of-nanocrystalline-ferrites-and-other-complex-oxides-by-sol-gel-method-with-participation-of-auto-combustion" class="btn btn-primary btn-sm">Procedia</a> <a href="https://publications.waset.org/abstracts/19687.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">409</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">344</span> The Effect of Market Orientation on Business Performance of Auto Parts Industry</h5> <div class="card-body"> <p class="card-text"><strong>Authors:</strong> <a href="https://publications.waset.org/abstracts/search?q=Vithaya%20Intraphimol">Vithaya Intraphimol</a> </p> <p class="card-text"><strong>Abstract:</strong></p> The purpose of this study is to investigate the relationship between market orientation and business performance through innovations that include product innovation and process innovation. Auto parts and accessories companies in Thailand were used as sample for this investigation. Survey research with structured questionnaire was used as the key instrument in collecting the data. The structural equation modeling (SEM) was assigned test the hypotheses. The sample size in this study requires the minimum sample size of 200. The result found that competitor orientation, and interfunctional coordination has an effect on product innovation. Moreover, interfunctional coordination has an effect on process innovation, and return on asset. This indicates that within- firm coordination has crucial to firms’ performances. The implication for practice, firms should support interfunctional coordination that members of different functional areas of an organization communicate and work together for the creation of value to target buyers they may have better profitability. <p class="card-text"><strong>Keywords:</strong> <a href="https://publications.waset.org/abstracts/search?q=auto%20parts%20industry" title="auto parts industry">auto parts industry</a>, <a href="https://publications.waset.org/abstracts/search?q=business%20performance" title=" business performance"> business performance</a>, <a href="https://publications.waset.org/abstracts/search?q=innovations" title=" innovations"> innovations</a>, <a href="https://publications.waset.org/abstracts/search?q=market%20orientation" title=" market orientation"> market orientation</a> </p> <a href="https://publications.waset.org/abstracts/44217/the-effect-of-market-orientation-on-business-performance-of-auto-parts-industry" class="btn btn-primary btn-sm">Procedia</a> <a href="https://publications.waset.org/abstracts/44217.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">310</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">343</span> Constructing a Bayesian Network for Solar Energy in Egypt Using Life Cycle Analysis and Machine Learning Algorithms</h5> <div class="card-body"> <p class="card-text"><strong>Authors:</strong> <a href="https://publications.waset.org/abstracts/search?q=Rawaa%20H.%20El-Bidweihy">Rawaa H. El-Bidweihy</a>, <a href="https://publications.waset.org/abstracts/search?q=Hisham%20M.%20Abdelsalam"> Hisham M. Abdelsalam</a>, <a href="https://publications.waset.org/abstracts/search?q=Ihab%20A.%20El-Khodary"> Ihab A. El-Khodary</a> </p> <p class="card-text"><strong>Abstract:</strong></p> In an era where machines run and shape our world, the need for a stable, non-ending source of energy emerges. In this study, the focus was on the solar energy in Egypt as a renewable source, the most important factors that could affect the solar energy’s market share throughout its life cycle production were analyzed and filtered, the relationships between them were derived before structuring a Bayesian network. Also, forecasted models were built for multiple factors to predict the states in Egypt by 2035, based on historical data and patterns, to be used as the nodes’ states in the network. 37 factors were found to might have an impact on the use of solar energy and then were deducted to 12 factors that were chosen to be the most effective to the solar energy’s life cycle in Egypt, based on surveying experts and data analysis, some of the factors were found to be recurring in multiple stages. The presented Bayesian network could be used later for scenario and decision analysis of using solar energy in Egypt, as a stable renewable source for generating any type of energy needed. <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=auto%20correlation" title=" auto correlation"> auto correlation</a>, <a href="https://publications.waset.org/abstracts/search?q=Bayesian%20network" title=" Bayesian network"> Bayesian network</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=life%20cycle" title=" life cycle"> life cycle</a>, <a href="https://publications.waset.org/abstracts/search?q=partial%20correlation" title=" partial correlation"> partial correlation</a>, <a href="https://publications.waset.org/abstracts/search?q=renewable%20energy" title=" renewable energy"> renewable energy</a>, <a href="https://publications.waset.org/abstracts/search?q=SARIMA" title=" SARIMA"> SARIMA</a>, <a href="https://publications.waset.org/abstracts/search?q=solar%20energy" title=" solar energy"> solar energy</a> </p> <a href="https://publications.waset.org/abstracts/127931/constructing-a-bayesian-network-for-solar-energy-in-egypt-using-life-cycle-analysis-and-machine-learning-algorithms" class="btn btn-primary btn-sm">Procedia</a> <a href="https://publications.waset.org/abstracts/127931.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">155</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 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