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model</title> <meta name="description" content="Search results for: forecasting model"> <meta name="keywords" content="forecasting model"> <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 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action="https://publications.waset.org/abstracts/search"> <div 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="forecasting model"> <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> 17016</div> </div> </div> </div> <h1 class="mt-3 mb-3 text-center" style="font-size:1.6rem;">Search results for: forecasting model</h1> <div class="card paper-listing mb-3 mt-3"> <h5 class="card-header" style="font-size:.9rem"><span class="badge badge-info">17016</span> A New Model for Production Forecasting in ERP</h5> <div class="card-body"> <p class="card-text"><strong>Authors:</strong> <a href="https://publications.waset.org/abstracts/search?q=S.%20F.%20Wong">S. F. Wong</a>, <a href="https://publications.waset.org/abstracts/search?q=W.%20I.%20Ho"> W. I. Ho</a>, <a href="https://publications.waset.org/abstracts/search?q=B.%20Lin"> B. Lin</a>, <a href="https://publications.waset.org/abstracts/search?q=Q.%20Huang"> Q. Huang</a> </p> <p class="card-text"><strong>Abstract:</strong></p> ERP has been used in many enterprises for management, the accuracy of the production forecasting module is vital to the decision making of the enterprise, and the profit is affected directly. Therefore, enhancing the accuracy of the production forecasting module can also increase the efficiency and profitability. To deal with a lot of data, a suitable, reliable and accurate statistics model is necessary. LSSVM and Grey System are two main models to be studied in this paper, and a case study is used to demonstrate how the combination model is effective to the result of forecasting. <p class="card-text"><strong>Keywords:</strong> <a href="https://publications.waset.org/abstracts/search?q=ERP" title="ERP">ERP</a>, <a href="https://publications.waset.org/abstracts/search?q=grey%20system" title=" grey system"> grey system</a>, <a href="https://publications.waset.org/abstracts/search?q=LSSVM" title=" LSSVM"> LSSVM</a>, <a href="https://publications.waset.org/abstracts/search?q=production%20forecasting" title=" production forecasting"> production forecasting</a> </p> <a href="https://publications.waset.org/abstracts/3348/a-new-model-for-production-forecasting-in-erp" class="btn btn-primary btn-sm">Procedia</a> <a href="https://publications.waset.org/abstracts/3348.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">462</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">17015</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">17014</span> Electricity Demand Modeling and Forecasting in Singapore</h5> <div class="card-body"> <p class="card-text"><strong>Authors:</strong> <a href="https://publications.waset.org/abstracts/search?q=Xian%20Li">Xian Li</a>, <a href="https://publications.waset.org/abstracts/search?q=Qing-Guo%20Wang"> Qing-Guo Wang</a>, <a href="https://publications.waset.org/abstracts/search?q=Jiangshuai%20Huang"> Jiangshuai Huang</a>, <a href="https://publications.waset.org/abstracts/search?q=Jidong%20Liu"> Jidong Liu</a>, <a href="https://publications.waset.org/abstracts/search?q=Ming%20Yu"> Ming Yu</a>, <a href="https://publications.waset.org/abstracts/search?q=Tan%20Kok%20Poh"> Tan Kok Poh </a> </p> <p class="card-text"><strong>Abstract:</strong></p> In power industry, accurate electricity demand forecasting for a certain leading time is important for system operation and control, etc. In this paper, we investigate the modeling and forecasting of Singapore’s electricity demand. Several standard models, such as HWT exponential smoothing model, the ARMA model and the ANNs model have been proposed based on historical demand data. We applied them to Singapore electricity market and proposed three refinements based on simulation to improve the modeling accuracy. Compared with existing models, our refined model can produce better forecasting accuracy. It is demonstrated in the simulation that by adding forecasting error into the forecasting equation, the modeling accuracy could be improved greatly. <p class="card-text"><strong>Keywords:</strong> <a href="https://publications.waset.org/abstracts/search?q=power%20industry" title="power industry">power industry</a>, <a href="https://publications.waset.org/abstracts/search?q=electricity%20demand" title=" electricity demand"> electricity demand</a>, <a href="https://publications.waset.org/abstracts/search?q=modeling" title=" modeling"> modeling</a>, <a href="https://publications.waset.org/abstracts/search?q=forecasting" title=" forecasting"> forecasting</a> </p> <a href="https://publications.waset.org/abstracts/13471/electricity-demand-modeling-and-forecasting-in-singapore" class="btn btn-primary btn-sm">Procedia</a> <a href="https://publications.waset.org/abstracts/13471.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">640</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">17013</span> Comparison of Applicability of Time Series Forecasting Models VAR, ARCH and ARMA in Management Science: Study Based on Empirical Analysis of Time Series Techniques</h5> <div class="card-body"> <p class="card-text"><strong>Authors:</strong> <a href="https://publications.waset.org/abstracts/search?q=Muhammad%20Tariq">Muhammad Tariq</a>, <a href="https://publications.waset.org/abstracts/search?q=Hammad%20Tahir"> Hammad Tahir</a>, <a href="https://publications.waset.org/abstracts/search?q=Fawwad%20Mahmood%20Butt"> Fawwad Mahmood Butt </a> </p> <p class="card-text"><strong>Abstract:</strong></p> Purpose: This study attempts to examine the best forecasting methodologies in the time series. The time series forecasting models such as VAR, ARCH and the ARMA are considered for the analysis. Methodology: The Bench Marks or the parameters such as Adjusted R square, F-stats, Durban Watson, and Direction of the roots have been critically and empirically analyzed. The empirical analysis consists of time series data of Consumer Price Index and Closing Stock Price. Findings: The results show that the VAR model performed better in comparison to other models. Both the reliability and significance of VAR model is highly appreciable. In contrary to it, the ARCH model showed very poor results for forecasting. However, the results of ARMA model appeared double standards i.e. the AR roots showed that model is stationary and that of MA roots showed that the model is invertible. Therefore, the forecasting would remain doubtful if it made on the bases of ARMA model. It has been concluded that VAR model provides best forecasting results. Practical Implications: This paper provides empirical evidences for the application of time series forecasting model. This paper therefore provides the base for the application of best time series forecasting model. <p class="card-text"><strong>Keywords:</strong> <a href="https://publications.waset.org/abstracts/search?q=forecasting" title="forecasting">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=auto%20regression" title=" auto regression"> auto regression</a>, <a href="https://publications.waset.org/abstracts/search?q=ARCH" title=" ARCH"> ARCH</a>, <a href="https://publications.waset.org/abstracts/search?q=ARMA" title=" ARMA"> ARMA</a> </p> <a href="https://publications.waset.org/abstracts/45124/comparison-of-applicability-of-time-series-forecasting-models-var-arch-and-arma-in-management-science-study-based-on-empirical-analysis-of-time-series-techniques" class="btn btn-primary btn-sm">Procedia</a> <a href="https://publications.waset.org/abstracts/45124.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">348</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">17012</span> Fuzzy Time Series Forecasting Based on Fuzzy Logical Relationships, PSO Technique, and Automatic Clustering Algorithm</h5> <div class="card-body"> <p class="card-text"><strong>Authors:</strong> <a href="https://publications.waset.org/abstracts/search?q=A.%20K.%20M.%20Kamrul%20Islam">A. K. M. Kamrul Islam</a>, <a href="https://publications.waset.org/abstracts/search?q=Abdelhamid%20Bouchachia"> Abdelhamid Bouchachia</a>, <a href="https://publications.waset.org/abstracts/search?q=Suang%20Cang"> Suang Cang</a>, <a href="https://publications.waset.org/abstracts/search?q=Hongnian%20Yu"> Hongnian Yu</a> </p> <p class="card-text"><strong>Abstract:</strong></p> Forecasting model has a great impact in terms of prediction and continues to do so into the future. Although many forecasting models have been studied in recent years, most researchers focus on different forecasting methods based on fuzzy time series to solve forecasting problems. The forecasted models accuracy fully depends on the two terms that are the length of the interval in the universe of discourse and the content of the forecast rules. Moreover, a hybrid forecasting method can be an effective and efficient way to improve forecasts rather than an individual forecasting model. There are different hybrids forecasting models which combined fuzzy time series with evolutionary algorithms, but the performances are not quite satisfactory. In this paper, we proposed a hybrid forecasting model which deals with the first order as well as high order fuzzy time series and particle swarm optimization to improve the forecasted accuracy. The proposed method used the historical enrollments of the University of Alabama as dataset in the forecasting process. Firstly, we considered an automatic clustering algorithm to calculate the appropriate interval for the historical enrollments. Then particle swarm optimization and fuzzy time series are combined that shows better forecasting accuracy than other existing forecasting models. <p class="card-text"><strong>Keywords:</strong> <a href="https://publications.waset.org/abstracts/search?q=fuzzy%20time%20series%20%28fts%29" title="fuzzy time series (fts)">fuzzy time series (fts)</a>, <a href="https://publications.waset.org/abstracts/search?q=particle%20swarm%20optimization" title=" particle swarm optimization"> particle swarm optimization</a>, <a href="https://publications.waset.org/abstracts/search?q=clustering%20algorithm" title=" clustering algorithm"> clustering algorithm</a>, <a href="https://publications.waset.org/abstracts/search?q=hybrid%20forecasting%20model" title=" hybrid forecasting model"> hybrid forecasting model</a> </p> <a href="https://publications.waset.org/abstracts/51515/fuzzy-time-series-forecasting-based-on-fuzzy-logical-relationships-pso-technique-and-automatic-clustering-algorithm" class="btn btn-primary btn-sm">Procedia</a> <a href="https://publications.waset.org/abstracts/51515.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">250</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">17011</span> Forecasting Stock Prices Based on the Residual Income Valuation Model: Evidence from a Time-Series Approach</h5> <div class="card-body"> <p class="card-text"><strong>Authors:</strong> <a href="https://publications.waset.org/abstracts/search?q=Chen-Yin%20Kuo">Chen-Yin Kuo</a>, <a href="https://publications.waset.org/abstracts/search?q=Yung-Hsin%20Lee"> Yung-Hsin Lee</a> </p> <p class="card-text"><strong>Abstract:</strong></p> Previous studies applying residual income valuation (RIV) model generally use panel data and single-equation model to forecast stock prices. Unlike these, this paper uses Taiwan longitudinal data to estimate multi-equation time-series models such as Vector Autoregressive (VAR), Vector Error Correction Model (VECM), and conduct out-of-sample forecasting. Further, this work assesses their forecasting performance by two instruments. In favor of extant research, the major finding shows that VECM outperforms other three models in forecasting for three stock sectors over entire horizons. It implies that an error correction term containing long-run information contributes to improve forecasting accuracy. Moreover, the pattern of composite shows that at longer horizon, VECM produces the greater reduction in errors, and performs substantially better than VAR. <p class="card-text"><strong>Keywords:</strong> <a href="https://publications.waset.org/abstracts/search?q=residual%20income%20valuation%20model" title="residual income valuation model">residual income valuation model</a>, <a href="https://publications.waset.org/abstracts/search?q=vector%20error%20correction%20model" title=" vector error correction model"> vector error correction model</a>, <a href="https://publications.waset.org/abstracts/search?q=out%20of%20sample%20forecasting" title=" out of sample forecasting"> out of sample forecasting</a>, <a href="https://publications.waset.org/abstracts/search?q=forecasting%20accuracy" title=" forecasting accuracy"> forecasting accuracy</a> </p> <a href="https://publications.waset.org/abstracts/1668/forecasting-stock-prices-based-on-the-residual-income-valuation-model-evidence-from-a-time-series-approach" class="btn btn-primary btn-sm">Procedia</a> <a href="https://publications.waset.org/abstracts/1668.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">316</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">17010</span> Forecasting Unemployment Rate in Selected European Countries Using Smoothing Methods</h5> <div class="card-body"> <p class="card-text"><strong>Authors:</strong> <a href="https://publications.waset.org/abstracts/search?q=Ksenija%20Dumi%C4%8Di%C4%87">Ksenija Dumičić</a>, <a href="https://publications.waset.org/abstracts/search?q=Anita%20%C4%8Ceh%20%C4%8Casni"> Anita Čeh Časni</a>, <a href="https://publications.waset.org/abstracts/search?q=Berislav%20%C5%BDmuk"> Berislav Žmuk</a> </p> <p class="card-text"><strong>Abstract:</strong></p> The aim of this paper is to select the most accurate forecasting method for predicting the future values of the unemployment rate in selected European countries. In order to do so, several forecasting techniques adequate for forecasting time series with trend component, were selected, namely: double exponential smoothing (also known as Holt`s method) and Holt-Winters` method which accounts for trend and seasonality. The results of the empirical analysis showed that the optimal model for forecasting unemployment rate in Greece was Holt-Winters` additive method. In the case of Spain, according to MAPE, the optimal model was double exponential smoothing model. Furthermore, for Croatia and Italy the best forecasting model for unemployment rate was Holt-Winters` multiplicative model, whereas in the case of Portugal the best model to forecast unemployment rate was Double exponential smoothing model. Our findings are in line with European Commission unemployment rate estimates. <p class="card-text"><strong>Keywords:</strong> <a href="https://publications.waset.org/abstracts/search?q=European%20Union%20countries" title="European Union countries">European Union countries</a>, <a href="https://publications.waset.org/abstracts/search?q=exponential%20smoothing%20methods" title=" exponential smoothing methods"> exponential smoothing methods</a>, <a href="https://publications.waset.org/abstracts/search?q=forecast%20accuracy%20unemployment%20rate" title=" forecast accuracy unemployment rate"> forecast accuracy unemployment rate</a> </p> <a href="https://publications.waset.org/abstracts/18328/forecasting-unemployment-rate-in-selected-european-countries-using-smoothing-methods" class="btn btn-primary btn-sm">Procedia</a> <a href="https://publications.waset.org/abstracts/18328.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">369</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">17009</span> Artificial Neural Network-Based Short-Term Load Forecasting for Mymensingh Area of Bangladesh</h5> <div class="card-body"> <p class="card-text"><strong>Authors:</strong> <a href="https://publications.waset.org/abstracts/search?q=S.%20M.%20Anowarul%20Haque">S. M. Anowarul Haque</a>, <a href="https://publications.waset.org/abstracts/search?q=Md.%20Asiful%20Islam"> Md. Asiful Islam</a> </p> <p class="card-text"><strong>Abstract:</strong></p> Electrical load forecasting is considered to be one of the most indispensable parts of a modern-day electrical power system. To ensure a reliable and efficient supply of electric energy, special emphasis should have been put on the predictive feature of electricity supply. Artificial Neural Network-based approaches have emerged to be a significant area of interest for electric load forecasting research. This paper proposed an Artificial Neural Network model based on the particle swarm optimization algorithm for improved electric load forecasting for Mymensingh, Bangladesh. The forecasting model is developed and simulated on the MATLAB environment with a large number of training datasets. The model is trained based on eight input parameters including historical load and weather data. The predicted load data are then compared with an available dataset for validation. The proposed neural network model is proved to be more reliable in terms of day-wise load forecasting for Mymensingh, Bangladesh. <p class="card-text"><strong>Keywords:</strong> <a href="https://publications.waset.org/abstracts/search?q=load%20forecasting" title="load forecasting">load forecasting</a>, <a href="https://publications.waset.org/abstracts/search?q=artificial%20neural%20network" title=" artificial neural network"> artificial neural network</a>, <a href="https://publications.waset.org/abstracts/search?q=particle%20swarm%20optimization" title=" particle swarm optimization"> particle swarm optimization</a> </p> <a href="https://publications.waset.org/abstracts/133841/artificial-neural-network-based-short-term-load-forecasting-for-mymensingh-area-of-bangladesh" class="btn btn-primary btn-sm">Procedia</a> <a href="https://publications.waset.org/abstracts/133841.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">171</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">17008</span> Forecasting Model to Predict Dengue Incidence in Malaysia </h5> <div class="card-body"> <p class="card-text"><strong>Authors:</strong> <a href="https://publications.waset.org/abstracts/search?q=W.%20H.%20Wan%20Zakiyatussariroh">W. H. Wan Zakiyatussariroh</a>, <a href="https://publications.waset.org/abstracts/search?q=A.%20A.%20Nasuhar"> A. A. Nasuhar</a>, <a href="https://publications.waset.org/abstracts/search?q=W.%20Y.%20Wan%20Fairos"> W. Y. Wan Fairos</a>, <a href="https://publications.waset.org/abstracts/search?q=Z.%20A.%20Nazatul%20Shahreen"> Z. A. Nazatul Shahreen</a> </p> <p class="card-text"><strong>Abstract:</strong></p> Forecasting dengue incidence in a population can provide useful information to facilitate the planning of the public health intervention. Many studies on dengue cases in Malaysia were conducted but are limited in modeling the outbreak and forecasting incidence. This article attempts to propose the most appropriate time series model to explain the behavior of dengue incidence in Malaysia for the purpose of forecasting future dengue outbreaks. Several seasonal auto-regressive integrated moving average (SARIMA) models were developed to model Malaysia’s number of dengue incidence on weekly data collected from January 2001 to December 2011. SARIMA (2,1,1)(1,1,1)52 model was found to be the most suitable model for Malaysia’s dengue incidence with the least value of Akaike information criteria (AIC) and Bayesian information criteria (BIC) for in-sample fitting. The models further evaluate out-sample forecast accuracy using four different accuracy measures. The results indicate that SARIMA (2,1,1)(1,1,1)52 performed well for both in-sample fitting and out-sample evaluation. <p class="card-text"><strong>Keywords:</strong> <a href="https://publications.waset.org/abstracts/search?q=time%20series%20modeling" title="time series modeling">time series modeling</a>, <a href="https://publications.waset.org/abstracts/search?q=Box-Jenkins" title=" Box-Jenkins"> Box-Jenkins</a>, <a href="https://publications.waset.org/abstracts/search?q=SARIMA" title=" SARIMA"> SARIMA</a>, <a href="https://publications.waset.org/abstracts/search?q=forecasting" title=" forecasting"> forecasting</a> </p> <a href="https://publications.waset.org/abstracts/1823/forecasting-model-to-predict-dengue-incidence-in-malaysia" class="btn btn-primary btn-sm">Procedia</a> <a href="https://publications.waset.org/abstracts/1823.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">484</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">17007</span> The Ability of Forecasting the Term Structure of Interest Rates Based on Nelson-Siegel and Svensson Model</h5> <div class="card-body"> <p class="card-text"><strong>Authors:</strong> <a href="https://publications.waset.org/abstracts/search?q=Tea%20Poklepovi%C4%87">Tea Poklepović</a>, <a href="https://publications.waset.org/abstracts/search?q=Zdravka%20Aljinovi%C4%87"> Zdravka Aljinović</a>, <a href="https://publications.waset.org/abstracts/search?q=Branka%20Marasovi%C4%87"> Branka Marasović</a> </p> <p class="card-text"><strong>Abstract:</strong></p> Due to the importance of yield curve and its estimation it is inevitable to have valid methods for yield curve forecasting in cases when there are scarce issues of securities and/or week trade on a secondary market. Therefore in this paper, after the estimation of weekly yield curves on Croatian financial market from October 2011 to August 2012 using Nelson-Siegel and Svensson models, yield curves are forecasted using Vector auto-regressive model and Neural networks. In general, it can be concluded that both forecasting methods have good prediction abilities where forecasting of yield curves based on Nelson Siegel estimation model give better results in sense of lower Mean Squared Error than forecasting based on Svensson model Also, in this case Neural networks provide slightly better results. Finally, it can be concluded that most appropriate way of yield curve prediction is neural networks using Nelson-Siegel estimation of yield curves. <p class="card-text"><strong>Keywords:</strong> <a href="https://publications.waset.org/abstracts/search?q=Nelson-Siegel%20Model" title="Nelson-Siegel Model">Nelson-Siegel Model</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=Svensson%20Model" title=" Svensson Model"> Svensson Model</a>, <a href="https://publications.waset.org/abstracts/search?q=vector%20autoregressive%20model" title=" vector autoregressive model"> vector autoregressive model</a>, <a href="https://publications.waset.org/abstracts/search?q=yield%20curve" title=" yield curve"> yield curve</a> </p> <a href="https://publications.waset.org/abstracts/2460/the-ability-of-forecasting-the-term-structure-of-interest-rates-based-on-nelson-siegel-and-svensson-model" class="btn btn-primary btn-sm">Procedia</a> <a href="https://publications.waset.org/abstracts/2460.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">333</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">17006</span> Estimation and Forecasting with a Quantile AR Model for Financial Returns </h5> <div class="card-body"> <p class="card-text"><strong>Authors:</strong> <a href="https://publications.waset.org/abstracts/search?q=Yuzhi%20Cai">Yuzhi Cai</a> </p> <p class="card-text"><strong>Abstract:</strong></p> This talk presents a Bayesian approach to quantile autoregressive (QAR) time series model estimation and forecasting. We establish that the joint posterior distribution of the model parameters and future values is well defined. The associated MCMC algorithm for parameter estimation and forecasting converges to the posterior distribution quickly. We also present a combining forecasts technique to produce more accurate out-of-sample forecasts by using a weighted sequence of fitted QAR models. A moving window method to check the quality of the estimated conditional quantiles is developed. We verify our methodology using simulation studies and then apply it to currency exchange rate data. An application of the method to the USD to GBP daily currency exchange rates will also be discussed. The results obtained show that an unequally weighted combining method performs better than other forecasting methodology. <p class="card-text"><strong>Keywords:</strong> <a href="https://publications.waset.org/abstracts/search?q=combining%20forecasts" title="combining forecasts">combining forecasts</a>, <a href="https://publications.waset.org/abstracts/search?q=MCMC" title=" MCMC"> MCMC</a>, <a href="https://publications.waset.org/abstracts/search?q=quantile%20modelling" title=" quantile modelling"> quantile modelling</a>, <a href="https://publications.waset.org/abstracts/search?q=quantile%20forecasting" title=" quantile forecasting"> quantile forecasting</a>, <a href="https://publications.waset.org/abstracts/search?q=predictive%20density%20functions" title=" predictive density functions"> predictive density functions</a> </p> <a href="https://publications.waset.org/abstracts/33437/estimation-and-forecasting-with-a-quantile-ar-model-for-financial-returns" class="btn btn-primary btn-sm">Procedia</a> <a href="https://publications.waset.org/abstracts/33437.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">347</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">17005</span> Currency Exchange Rate Forecasts Using Quantile Regression</h5> <div class="card-body"> <p class="card-text"><strong>Authors:</strong> <a href="https://publications.waset.org/abstracts/search?q=Yuzhi%20Cai">Yuzhi Cai</a> </p> <p class="card-text"><strong>Abstract:</strong></p> In this paper, we discuss a Bayesian approach to quantile autoregressive (QAR) time series model estimation and forecasting. Together with a combining forecasts technique, we then predict USD to GBP currency exchange rates. Combined forecasts contain all the information captured by the fitted QAR models at different quantile levels and are therefore better than those obtained from individual models. Our results show that an unequally weighted combining method performs better than other forecasting methodology. We found that a median AR model can perform well in point forecasting when the predictive density functions are symmetric. However, in practice, using the median AR model alone may involve the loss of information about the data captured by other QAR models. We recommend that combined forecasts should be used whenever possible. <p class="card-text"><strong>Keywords:</strong> <a href="https://publications.waset.org/abstracts/search?q=combining%20forecasts" title="combining forecasts">combining forecasts</a>, <a href="https://publications.waset.org/abstracts/search?q=MCMC" title=" MCMC"> MCMC</a>, <a href="https://publications.waset.org/abstracts/search?q=predictive%20density%20functions" title=" predictive density functions"> predictive density functions</a>, <a href="https://publications.waset.org/abstracts/search?q=quantile%20forecasting" title=" quantile forecasting"> quantile forecasting</a>, <a href="https://publications.waset.org/abstracts/search?q=quantile%20modelling" title=" quantile modelling"> quantile modelling</a> </p> <a href="https://publications.waset.org/abstracts/45531/currency-exchange-rate-forecasts-using-quantile-regression" class="btn btn-primary btn-sm">Procedia</a> <a href="https://publications.waset.org/abstracts/45531.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">256</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">17004</span> EarlyWarning for Financial Stress Events:A Credit-Regime Switching Approach</h5> <div class="card-body"> <p class="card-text"><strong>Authors:</strong> <a href="https://publications.waset.org/abstracts/search?q=Fuchun%20Li">Fuchun Li</a>, <a href="https://publications.waset.org/abstracts/search?q=Hong%20Xiao"> Hong Xiao </a> </p> <p class="card-text"><strong>Abstract:</strong></p> We propose a new early warning model for predicting financial stress events for a given future time. In this model, we examine whether credit conditions play an important role as a nonlinear propagator of shocks when predicting the likelihood of occurrence of financial stress events for a given future time. This propagation takes the form of a threshold regression in which a regime change occurs if credit conditions cross a critical threshold. Given the new early warning model for financial stress events, we evaluate the performance of this model and currently available alternatives, such as the model from signal extraction approach, and linear regression model. In-sample forecasting results indicate that the three types of models are useful tools for predicting financial stress events while none of them outperforms others across all criteria considered. The out-of-sample forecasting results suggest that the credit-regime switching model performs better than the two others across all criteria and all forecasting horizons considered. <p class="card-text"><strong>Keywords:</strong> <a href="https://publications.waset.org/abstracts/search?q=cut-off%20probability" title="cut-off probability">cut-off probability</a>, <a href="https://publications.waset.org/abstracts/search?q=early%20warning%20model" title=" early warning model"> early warning model</a>, <a href="https://publications.waset.org/abstracts/search?q=financial%20crisis" title=" financial crisis"> financial crisis</a>, <a href="https://publications.waset.org/abstracts/search?q=financial%20stress" title=" financial stress"> financial stress</a>, <a href="https://publications.waset.org/abstracts/search?q=regime-switching%20model" title=" regime-switching model"> regime-switching model</a>, <a href="https://publications.waset.org/abstracts/search?q=forecasting%20horizons" title=" forecasting horizons "> forecasting horizons </a> </p> <a href="https://publications.waset.org/abstracts/33040/earlywarning-for-financial-stress-eventsa-credit-regime-switching-approach" class="btn btn-primary btn-sm">Procedia</a> <a href="https://publications.waset.org/abstracts/33040.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">435</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">17003</span> Mixed Effects Models for Short-Term Load Forecasting for the Spanish Regions: Castilla-Leon, Castilla-La Mancha and Andalucia</h5> <div class="card-body"> <p class="card-text"><strong>Authors:</strong> <a href="https://publications.waset.org/abstracts/search?q=C.%20Senabre">C. Senabre</a>, <a href="https://publications.waset.org/abstracts/search?q=S.%20Valero"> S. Valero</a>, <a href="https://publications.waset.org/abstracts/search?q=M.%20Lopez"> M. Lopez</a>, <a href="https://publications.waset.org/abstracts/search?q=E.%20Velasco"> E. Velasco</a>, <a href="https://publications.waset.org/abstracts/search?q=M.%20Sanchez"> M. Sanchez</a> </p> <p class="card-text"><strong>Abstract:</strong></p> This paper focuses on an application of linear mixed models to short-term load forecasting. The challenge of this research is to improve a currently working model at the Spanish Transport System Operator, programmed by us, and based on linear autoregressive techniques and neural networks. The forecasting system currently forecasts each of the regions within the Spanish grid separately, even though the behavior of the load in each region is affected by the same factors in a similar way. A load forecasting system has been verified in this work by using the real data from a utility. In this research it has been used an integration of several regions into a linear mixed model as starting point to obtain the information from other regions. Firstly, the systems to learn general behaviors present in all regions, and secondly, it is identified individual deviation in each regions. The technique can be especially useful when modeling the effect of special days with scarce information from the past. The three most relevant regions of the system have been used to test the model, focusing on special day and improving the performance of both currently working models used as benchmark. A range of comparisons with different forecasting models has been conducted. The forecasting results demonstrate the superiority of the proposed methodology. <p class="card-text"><strong>Keywords:</strong> <a href="https://publications.waset.org/abstracts/search?q=short-term%20load%20forecasting" title="short-term load forecasting">short-term load forecasting</a>, <a href="https://publications.waset.org/abstracts/search?q=mixed%20effects%20models" title=" mixed effects models"> mixed effects models</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=mixed%20effects%20models" title=" mixed effects models"> mixed effects models</a> </p> <a href="https://publications.waset.org/abstracts/100166/mixed-effects-models-for-short-term-load-forecasting-for-the-spanish-regions-castilla-leon-castilla-la-mancha-and-andalucia" class="btn btn-primary btn-sm">Procedia</a> <a href="https://publications.waset.org/abstracts/100166.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">189</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">17002</span> Forecasting Silver Commodity Prices Using Geometric Brownian Motion: A Stochastic Approach</h5> <div class="card-body"> <p class="card-text"><strong>Authors:</strong> <a href="https://publications.waset.org/abstracts/search?q=Sina%20Dehghani">Sina Dehghani</a>, <a href="https://publications.waset.org/abstracts/search?q=Zhikang%20Rong"> Zhikang Rong</a> </p> <p class="card-text"><strong>Abstract:</strong></p> Historically, a variety of approaches have been taken to forecast commodity prices due to the significant implications of these values on the global economy. An accurate forecasting tool for a valuable commodity would significantly benefit investors and governmental agencies. Silver, in particular, has grown significantly as a commodity in recent years due to its use in healthcare and technology. This manuscript aims to utilize the Geometric Brownian Motion predictive model to forecast silver commodity prices over multiple 3-year periods. The results of the study indicate that the model has several limitations, particularly its inability to work effectively over longer periods of time, but still was extremely effective over shorter time frames. This study sets a baseline for silver commodity forecasting with GBM, and the model could be further strengthened with refinement. <p class="card-text"><strong>Keywords:</strong> <a href="https://publications.waset.org/abstracts/search?q=geometric%20Brownian%20motion" title="geometric Brownian motion">geometric Brownian motion</a>, <a href="https://publications.waset.org/abstracts/search?q=commodity" title=" commodity"> commodity</a>, <a href="https://publications.waset.org/abstracts/search?q=risk%20management" title=" risk management"> risk management</a>, <a href="https://publications.waset.org/abstracts/search?q=volatility" title=" volatility"> volatility</a>, <a href="https://publications.waset.org/abstracts/search?q=stochastic%20behavior" title=" stochastic behavior"> stochastic behavior</a>, <a href="https://publications.waset.org/abstracts/search?q=price%20forecasting" title=" price forecasting"> price forecasting</a> </p> <a href="https://publications.waset.org/abstracts/192474/forecasting-silver-commodity-prices-using-geometric-brownian-motion-a-stochastic-approach" class="btn btn-primary btn-sm">Procedia</a> <a href="https://publications.waset.org/abstracts/192474.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">23</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">17001</span> Improving Forecasting Demand for Maintenance Spare Parts: Case Study</h5> <div class="card-body"> <p class="card-text"><strong>Authors:</strong> <a href="https://publications.waset.org/abstracts/search?q=Abdulaziz%20Afandi">Abdulaziz Afandi</a> </p> <p class="card-text"><strong>Abstract:</strong></p> Minimizing the inventory cost, optimizing the inventory quantities, and increasing system operational availability are the main motivations to enhance forecasting demand of spare parts in a major power utility company in Medina. This paper reports in an effort made to optimize the orders quantities of spare parts by improving the method of forecasting the demand. The study focuses on equipment that has frequent spare parts purchase orders with uncertain demand. The pattern of the demand considers a lumpy pattern which makes conventional forecasting methods less effective. A comparison was made by benchmarking various methods of forecasting based on experts’ criteria to select the most suitable method for the case study. Three actual data sets were used to make the forecast in this case study. Two neural networks (NN) approaches were utilized and compared, namely long short-term memory (LSTM) and multilayer perceptron (MLP). The results as expected, showed that the NN models gave better results than traditional forecasting method (judgmental method). In addition, the LSTM model had a higher predictive accuracy than the MLP model. <p class="card-text"><strong>Keywords:</strong> <a href="https://publications.waset.org/abstracts/search?q=neural%20network" title="neural network">neural network</a>, <a href="https://publications.waset.org/abstracts/search?q=LSTM" title=" LSTM"> LSTM</a>, <a href="https://publications.waset.org/abstracts/search?q=MLP" title=" MLP"> MLP</a>, <a href="https://publications.waset.org/abstracts/search?q=forecasting%20demand" title=" forecasting demand"> forecasting demand</a>, <a href="https://publications.waset.org/abstracts/search?q=inventory%20management" title=" inventory management"> inventory management</a> </p> <a href="https://publications.waset.org/abstracts/148574/improving-forecasting-demand-for-maintenance-spare-parts-case-study" class="btn btn-primary btn-sm">Procedia</a> <a href="https://publications.waset.org/abstracts/148574.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">127</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">17000</span> Short Term Distribution Load Forecasting Using Wavelet Transform and Artificial Neural Networks</h5> <div class="card-body"> <p class="card-text"><strong>Authors:</strong> <a href="https://publications.waset.org/abstracts/search?q=S.%20Neelima">S. Neelima</a>, <a href="https://publications.waset.org/abstracts/search?q=P.%20S.%20Subramanyam"> P. S. Subramanyam</a> </p> <p class="card-text"><strong>Abstract:</strong></p> The major tool for distribution planning is load forecasting, which is the anticipation of the load in advance. Artificial neural networks have found wide applications in load forecasting to obtain an efficient strategy for planning and management. In this paper, the application of neural networks to study the design of short term load forecasting (STLF) Systems was explored. Our work presents a pragmatic methodology for short term load forecasting (STLF) using proposed two-stage model of wavelet transform (WT) and artificial neural network (ANN). It is a two-stage prediction system which involves wavelet decomposition of input data at the first stage and the decomposed data with another input is trained using a separate neural network to forecast the load. The forecasted load is obtained by reconstruction of the decomposed data. The hybrid model has been trained and validated using load data from Telangana State Electricity Board. <p class="card-text"><strong>Keywords:</strong> <a href="https://publications.waset.org/abstracts/search?q=electrical%20distribution%20systems" title="electrical distribution systems">electrical distribution systems</a>, <a href="https://publications.waset.org/abstracts/search?q=wavelet%20transform%20%28WT%29" title=" wavelet transform (WT)"> wavelet transform (WT)</a>, <a href="https://publications.waset.org/abstracts/search?q=short%20term%20load%20forecasting%20%28STLF%29" title=" short term load forecasting (STLF)"> short term load forecasting (STLF)</a>, <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> </p> <a href="https://publications.waset.org/abstracts/34572/short-term-distribution-load-forecasting-using-wavelet-transform-and-artificial-neural-networks" class="btn btn-primary btn-sm">Procedia</a> <a href="https://publications.waset.org/abstracts/34572.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">436</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">16999</span> The Impact of Artificial Intelligence on Spare Parts Technology</h5> <div class="card-body"> <p class="card-text"><strong>Authors:</strong> <a href="https://publications.waset.org/abstracts/search?q=Amir%20Andria%20Gad%20Shehata">Amir Andria Gad Shehata</a> </p> <p class="card-text"><strong>Abstract:</strong></p> Minimizing the inventory cost, optimizing the inventory quantities, and increasing system operational availability are the main motivations to enhance forecasting demand of spare parts in a major power utility company in Medina. This paper reports in an effort made to optimize the orders quantities of spare parts by improving the method of forecasting the demand. The study focuses on equipment that has frequent spare parts purchase orders with uncertain demand. The pattern of the demand considers a lumpy pattern which makes conventional forecasting methods less effective. A comparison was made by benchmarking various methods of forecasting based on experts’ criteria to select the most suitable method for the case study. Three actual data sets were used to make the forecast in this case study. Two neural networks (NN) approaches were utilized and compared, namely long short-term memory (LSTM) and multilayer perceptron (MLP). The results as expected, showed that the NN models gave better results than traditional forecasting method (judgmental method). In addition, the LSTM model had a higher predictive accuracy than the MLP model. <p class="card-text"><strong>Keywords:</strong> <a href="https://publications.waset.org/abstracts/search?q=spare%20part" title="spare part">spare part</a>, <a href="https://publications.waset.org/abstracts/search?q=spare%20part%20inventory" title=" spare part inventory"> spare part inventory</a>, <a href="https://publications.waset.org/abstracts/search?q=inventory%20model" title=" inventory model"> inventory model</a>, <a href="https://publications.waset.org/abstracts/search?q=optimization" title=" optimization"> optimization</a>, <a href="https://publications.waset.org/abstracts/search?q=maintenanceneural%20network" title=" maintenanceneural network"> maintenanceneural network</a>, <a href="https://publications.waset.org/abstracts/search?q=LSTM" title=" LSTM"> LSTM</a>, <a href="https://publications.waset.org/abstracts/search?q=MLP" title=" MLP"> MLP</a>, <a href="https://publications.waset.org/abstracts/search?q=forecasting%20demand" title=" forecasting demand"> forecasting demand</a>, <a href="https://publications.waset.org/abstracts/search?q=inventory%20management" title=" inventory management"> inventory management</a> </p> <a href="https://publications.waset.org/abstracts/184674/the-impact-of-artificial-intelligence-on-spare-parts-technology" class="btn btn-primary btn-sm">Procedia</a> <a href="https://publications.waset.org/abstracts/184674.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">63</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">16998</span> A Case Study of Typhoon Tracks: Insights from the Interaction between Typhoon Hinnamnor and Ocean Currents in 2022</h5> <div class="card-body"> <p class="card-text"><strong>Authors:</strong> <a href="https://publications.waset.org/abstracts/search?q=Wei-Kuo%20Soong">Wei-Kuo Soong</a> </p> <p class="card-text"><strong>Abstract:</strong></p> The forecasting of typhoon tracks remains a formidable challenge, primarily attributable to the paucity of observational data in the open sea and the intricate influence of weather systems at varying scales. This study investigates the case of Typhoon Hinnamnor in 2022, examining its trajectory and intensity fluctuations in relation to the interaction with a concurrent tropical cyclone and sea surface temperatures (SST). Utilizing the Weather Research and Forecasting Model (WRF), to simulate and analyze the interaction between Typhoon Hinnamnor and its environmental factors, shedding light on the mechanisms driving typhoon development and enhancing forecasting capabilities. <p class="card-text"><strong>Keywords:</strong> <a href="https://publications.waset.org/abstracts/search?q=typhoon" title="typhoon">typhoon</a>, <a href="https://publications.waset.org/abstracts/search?q=sea%20surface%20temperature" title=" sea surface temperature"> sea surface temperature</a>, <a href="https://publications.waset.org/abstracts/search?q=forecasting" title=" forecasting"> forecasting</a>, <a href="https://publications.waset.org/abstracts/search?q=WRF" title=" WRF"> WRF</a> </p> <a href="https://publications.waset.org/abstracts/176526/a-case-study-of-typhoon-tracks-insights-from-the-interaction-between-typhoon-hinnamnor-and-ocean-currents-in-2022" class="btn btn-primary btn-sm">Procedia</a> <a href="https://publications.waset.org/abstracts/176526.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">52</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">16997</span> Composite Forecasts Accuracy for Automobile Sales in Thailand</h5> <div class="card-body"> <p class="card-text"><strong>Authors:</strong> <a href="https://publications.waset.org/abstracts/search?q=Watchareeporn%20Chaimongkol">Watchareeporn Chaimongkol</a> </p> <p class="card-text"><strong>Abstract:</strong></p> In this paper, we compare the statistical measures accuracy of composite forecasting model to estimate automobile customer demand in Thailand. A modified simple exponential smoothing and autoregressive integrate moving average (ARIMA) forecasting model is built to estimate customer demand of passenger cars, instead of using information of historical sales data. Our model takes into account special characteristic of the Thai automobile market such as sales promotion, advertising and publicity, petrol price, and interest rate for loan. We evaluate our forecasting model by comparing forecasts with actual data using six accuracy measurements, mean absolute percentage error (MAPE), geometric mean absolute error (GMAE), symmetric mean absolute percentage error (sMAPE), mean absolute scaled error (MASE), median relative absolute error (MdRAE), and geometric mean relative absolute error (GMRAE). <p class="card-text"><strong>Keywords:</strong> <a href="https://publications.waset.org/abstracts/search?q=composite%20forecasting" title="composite forecasting">composite forecasting</a>, <a href="https://publications.waset.org/abstracts/search?q=simple%20exponential%20smoothing%20model" title=" simple exponential smoothing model"> simple exponential smoothing model</a>, <a href="https://publications.waset.org/abstracts/search?q=autoregressive%20integrate%20moving%20average%20model%20selection" title=" autoregressive integrate moving average model selection"> autoregressive integrate moving average model selection</a>, <a href="https://publications.waset.org/abstracts/search?q=accuracy%20measurements" title=" accuracy measurements"> accuracy measurements</a> </p> <a href="https://publications.waset.org/abstracts/6189/composite-forecasts-accuracy-for-automobile-sales-in-thailand" class="btn btn-primary btn-sm">Procedia</a> <a href="https://publications.waset.org/abstracts/6189.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">362</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">16996</span> Forecasting Models for Steel Demand Uncertainty Using Bayesian Methods</h5> <div class="card-body"> <p class="card-text"><strong>Authors:</strong> <a href="https://publications.waset.org/abstracts/search?q=Watcharin%20Sangma">Watcharin Sangma</a>, <a href="https://publications.waset.org/abstracts/search?q=Onsiri%20Chanmuang"> Onsiri Chanmuang</a>, <a href="https://publications.waset.org/abstracts/search?q=Pitsanu%20Tongkhow"> Pitsanu Tongkhow</a> </p> <p class="card-text"><strong>Abstract:</strong></p> A forecasting model for steel demand uncertainty in Thailand is proposed. It consists of trend, autocorrelation, and outliers in a hierarchical Bayesian frame work. The proposed model uses a cumulative Weibull distribution function, latent first-order autocorrelation, and binary selection, to account for trend, time-varying autocorrelation, and outliers, respectively. The Gibbs sampling Markov Chain Monte Carlo (MCMC) is used for parameter estimation. The proposed model is applied to steel demand index data in Thailand. The root mean square error (RMSE), mean absolute percentage error (MAPE), and mean absolute error (MAE) criteria are used for model comparison. The study reveals that the proposed model is more appropriate than the exponential smoothing method. <p class="card-text"><strong>Keywords:</strong> <a href="https://publications.waset.org/abstracts/search?q=forecasting%20model" title="forecasting model">forecasting model</a>, <a href="https://publications.waset.org/abstracts/search?q=steel%20demand%20uncertainty" title=" steel demand uncertainty"> steel demand uncertainty</a>, <a href="https://publications.waset.org/abstracts/search?q=hierarchical%20Bayesian%20framework" title=" hierarchical Bayesian framework"> hierarchical Bayesian framework</a>, <a href="https://publications.waset.org/abstracts/search?q=exponential%20smoothing%20method" title=" exponential smoothing method"> exponential smoothing method</a> </p> <a href="https://publications.waset.org/abstracts/10196/forecasting-models-for-steel-demand-uncertainty-using-bayesian-methods" class="btn btn-primary btn-sm">Procedia</a> <a href="https://publications.waset.org/abstracts/10196.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">350</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">16995</span> Mathematical Based Forecasting of Heart Attack</h5> <div class="card-body"> <p class="card-text"><strong>Authors:</strong> <a href="https://publications.waset.org/abstracts/search?q=Razieh%20Khalafi">Razieh Khalafi</a> </p> <p class="card-text"><strong>Abstract:</strong></p> Myocardial infarction (MI) or acute myocardial infarction (AMI), commonly known as a heart attack, occurs when blood flow stops to part of the heart causing damage to the heart muscle. An ECG can often show evidence of a previous heart attack or one that's in progress. The patterns on the ECG may indicate which part of your heart has been damaged, as well as the extent of the damage. In chaos theory, the correlation dimension is a measure of the dimensionality of the space occupied by a set of random points, often referred to as a type of fractal dimension. In this research by considering ECG signal as a random walk we work on forecasting the oncoming heart attack by analyzing the ECG signals using the correlation dimension. In order to test the model a set of ECG signals for patients before and after heart attack was used and the strength of model for forecasting the behavior of these signals were checked. Results shows this methodology can forecast the ECG and accordingly heart attack with high accuracy. <p class="card-text"><strong>Keywords:</strong> <a href="https://publications.waset.org/abstracts/search?q=heart%20attack" title="heart attack">heart attack</a>, <a href="https://publications.waset.org/abstracts/search?q=ECG" title=" ECG"> ECG</a>, <a href="https://publications.waset.org/abstracts/search?q=random%20walk" title=" random walk"> random walk</a>, <a href="https://publications.waset.org/abstracts/search?q=correlation%20dimension" title=" correlation dimension"> correlation dimension</a>, <a href="https://publications.waset.org/abstracts/search?q=forecasting" title=" forecasting"> forecasting</a> </p> <a href="https://publications.waset.org/abstracts/29782/mathematical-based-forecasting-of-heart-attack" class="btn btn-primary btn-sm">Procedia</a> <a href="https://publications.waset.org/abstracts/29782.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">540</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">16994</span> Load Forecasting in Short-Term Including Meteorological Variables for Balearic Islands Paper</h5> <div class="card-body"> <p class="card-text"><strong>Authors:</strong> <a href="https://publications.waset.org/abstracts/search?q=Carolina%20Senabre">Carolina Senabre</a>, <a href="https://publications.waset.org/abstracts/search?q=Sergio%20Valero"> Sergio Valero</a>, <a href="https://publications.waset.org/abstracts/search?q=Miguel%20Lopez"> Miguel Lopez</a>, <a href="https://publications.waset.org/abstracts/search?q=Antonio%20Gabaldon"> Antonio Gabaldon</a> </p> <p class="card-text"><strong>Abstract:</strong></p> This paper presents a comprehensive survey of the short-term load forecasting (STLF). Since the behavior of consumers and producers continue changing as new technologies, it is an ongoing process, and moreover, new policies become available. The results of a research study for the Spanish Transport System Operator (REE) is presented in this paper. It is presented the improvement of the forecasting accuracy in the Balearic Islands considering the introduction of meteorological variables, such as temperature to reduce forecasting error. Variables analyzed for the forecasting in terms of overall accuracy are cloudiness, solar radiation, and wind velocity. It has also been analyzed the type of days to be considered in the research. <p class="card-text"><strong>Keywords:</strong> <a href="https://publications.waset.org/abstracts/search?q=short-term%20load%20forecasting" title="short-term load forecasting">short-term load forecasting</a>, <a href="https://publications.waset.org/abstracts/search?q=power%20demand" title=" power demand"> power demand</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=load%20forecasting" title=" load forecasting"> load forecasting</a> </p> <a href="https://publications.waset.org/abstracts/107890/load-forecasting-in-short-term-including-meteorological-variables-for-balearic-islands-paper" class="btn btn-primary btn-sm">Procedia</a> <a href="https://publications.waset.org/abstracts/107890.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">190</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">16993</span> Performance Evaluation of the Classic seq2seq Model versus a Proposed Semi-supervised Long Short-Term Memory Autoencoder for Time Series Data Forecasting</h5> <div class="card-body"> <p class="card-text"><strong>Authors:</strong> <a href="https://publications.waset.org/abstracts/search?q=Aswathi%20Thrivikraman">Aswathi Thrivikraman</a>, <a href="https://publications.waset.org/abstracts/search?q=S.%20Advaith"> S. Advaith</a> </p> <p class="card-text"><strong>Abstract:</strong></p> The study is aimed at designing encoders for deciphering intricacies in time series data by redescribing the dynamics operating on a lower-dimensional manifold. A semi-supervised LSTM autoencoder is devised and investigated to see if the latent representation of the time series data can better forecast the data. End-to-end training of the LSTM autoencoder, together with another LSTM network that is connected to the latent space, forces the hidden states of the encoder to represent the most meaningful latent variables relevant for forecasting. Furthermore, the study compares the predictions with those of a traditional seq2seq model. <p class="card-text"><strong>Keywords:</strong> <a href="https://publications.waset.org/abstracts/search?q=LSTM" title="LSTM">LSTM</a>, <a href="https://publications.waset.org/abstracts/search?q=autoencoder" title=" autoencoder"> autoencoder</a>, <a href="https://publications.waset.org/abstracts/search?q=forecasting" title=" forecasting"> forecasting</a>, <a href="https://publications.waset.org/abstracts/search?q=seq2seq%20model" title=" seq2seq model"> seq2seq model</a> </p> <a href="https://publications.waset.org/abstracts/157449/performance-evaluation-of-the-classic-seq2seq-model-versus-a-proposed-semi-supervised-long-short-term-memory-autoencoder-for-time-series-data-forecasting" class="btn btn-primary btn-sm">Procedia</a> <a href="https://publications.waset.org/abstracts/157449.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> <div class="card paper-listing mb-3 mt-3"> <h5 class="card-header" style="font-size:.9rem"><span class="badge badge-info">16992</span> A New Mathematical Method for Heart Attack Forecasting</h5> <div class="card-body"> <p class="card-text"><strong>Authors:</strong> <a href="https://publications.waset.org/abstracts/search?q=Razi%20Khalafi">Razi Khalafi</a> </p> <p class="card-text"><strong>Abstract:</strong></p> Myocardial Infarction (MI) or acute Myocardial Infarction (AMI), commonly known as a heart attack, occurs when blood flow stops to part of the heart causing damage to the heart muscle. An ECG can often show evidence of a previous heart attack or one that's in progress. The patterns on the ECG may indicate which part of your heart has been damaged, as well as the extent of the damage. In chaos theory, the correlation dimension is a measure of the dimensionality of the space occupied by a set of random points, often referred to as a type of fractal dimension. In this research by considering ECG signal as a random walk we work on forecasting the oncoming heart attack by analysing the ECG signals using the correlation dimension. In order to test the model a set of ECG signals for patients before and after heart attack was used and the strength of model for forecasting the behaviour of these signals were checked. Results show this methodology can forecast the ECG and accordingly heart attack with high accuracy. <p class="card-text"><strong>Keywords:</strong> <a href="https://publications.waset.org/abstracts/search?q=heart%20attack" title="heart attack">heart attack</a>, <a href="https://publications.waset.org/abstracts/search?q=ECG" title=" ECG"> ECG</a>, <a href="https://publications.waset.org/abstracts/search?q=random%20walk" title=" random walk"> random walk</a>, <a href="https://publications.waset.org/abstracts/search?q=correlation%20dimension" title=" correlation dimension"> correlation dimension</a>, <a href="https://publications.waset.org/abstracts/search?q=forecasting" title=" forecasting"> forecasting</a> </p> <a href="https://publications.waset.org/abstracts/30802/a-new-mathematical-method-for-heart-attack-forecasting" class="btn btn-primary btn-sm">Procedia</a> <a href="https://publications.waset.org/abstracts/30802.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">506</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">16991</span> A Method of Effective Planning and Control of Industrial Facility Energy Consumption</h5> <div class="card-body"> <p class="card-text"><strong>Authors:</strong> <a href="https://publications.waset.org/abstracts/search?q=Aleksandra%20Aleksandrovna%20Filimonova">Aleksandra Aleksandrovna Filimonova</a>, <a href="https://publications.waset.org/abstracts/search?q=Lev%20Sergeevich%20Kazarinov"> Lev Sergeevich Kazarinov</a>, <a href="https://publications.waset.org/abstracts/search?q=Tatyana%20Aleksandrovna%20Barbasova"> Tatyana Aleksandrovna Barbasova</a> </p> <p class="card-text"><strong>Abstract:</strong></p> A method of effective planning and control of industrial facility energy consumption is offered. The method allows to optimally arrange the management and full control of complex production facilities in accordance with the criteria of minimal technical and economic losses at the forecasting control. The method is based on the optimal construction of the power efficiency characteristics with the prescribed accuracy. The problem of optimal designing of the forecasting model is solved on the basis of three criteria: maximizing the weighted sum of the points of forecasting with the prescribed accuracy; the solving of the problem by the standard principles at the incomplete statistic data on the basis of minimization of the regularized function; minimizing the technical and economic losses due to the forecasting errors. <p class="card-text"><strong>Keywords:</strong> <a href="https://publications.waset.org/abstracts/search?q=energy%20consumption" title="energy consumption">energy consumption</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=energy%20management%20system" title=" energy management system"> energy management system</a>, <a href="https://publications.waset.org/abstracts/search?q=forecasting%20model" title=" forecasting model"> forecasting model</a>, <a href="https://publications.waset.org/abstracts/search?q=power%20efficiency%20characteristics" title=" power efficiency characteristics"> power efficiency characteristics</a> </p> <a href="https://publications.waset.org/abstracts/38726/a-method-of-effective-planning-and-control-of-industrial-facility-energy-consumption" class="btn btn-primary btn-sm">Procedia</a> <a href="https://publications.waset.org/abstracts/38726.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">392</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">16990</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">16989</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">16988</span> Collaborative Planning and Forecasting</h5> <div class="card-body"> <p class="card-text"><strong>Authors:</strong> <a href="https://publications.waset.org/abstracts/search?q=Neha%20Asthana">Neha Asthana</a>, <a href="https://publications.waset.org/abstracts/search?q=Vishal%20Krishna%20Prasad"> Vishal Krishna Prasad</a> </p> <p class="card-text"><strong>Abstract:</strong></p> Collaborative planning and forecasting are the innovative and systematic approaches towards productive integration and assimilation of data synergized into information. The changing and variable market dynamics have persuaded global business chains to incorporate collaborative planning and forecasting as an imperative tool. Thus, it is essential for the supply chains to constantly improvise, update its nature, and mould as per changing global environment. <p class="card-text"><strong>Keywords:</strong> <a href="https://publications.waset.org/abstracts/search?q=information%20transfer" title="information transfer">information transfer</a>, <a href="https://publications.waset.org/abstracts/search?q=forecasting" title=" forecasting"> forecasting</a>, <a href="https://publications.waset.org/abstracts/search?q=optimization" title=" optimization"> optimization</a>, <a href="https://publications.waset.org/abstracts/search?q=supply%20chain%20management" title=" supply chain management"> supply chain management</a> </p> <a href="https://publications.waset.org/abstracts/7060/collaborative-planning-and-forecasting" class="btn btn-primary btn-sm">Procedia</a> <a href="https://publications.waset.org/abstracts/7060.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">435</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">16987</span> Development of a Wind Resource Assessment Framework Using Weather Research and Forecasting (WRF) Model, Python Scripting and Geographic Information Systems</h5> <div class="card-body"> <p class="card-text"><strong>Authors:</strong> <a href="https://publications.waset.org/abstracts/search?q=Jerome%20T.%20Tolentino">Jerome T. Tolentino</a>, <a href="https://publications.waset.org/abstracts/search?q=Ma.%20Victoria%20Rejuso"> Ma. Victoria Rejuso</a>, <a href="https://publications.waset.org/abstracts/search?q=Jara%20Kaye%20Villanueva"> Jara Kaye Villanueva</a>, <a href="https://publications.waset.org/abstracts/search?q=Loureal%20Camille%20Inocencio"> Loureal Camille Inocencio</a>, <a href="https://publications.waset.org/abstracts/search?q=Ma.%20Rosario%20Concepcion%20O.%20Ang"> Ma. Rosario Concepcion O. Ang</a> </p> <p class="card-text"><strong>Abstract:</strong></p> Wind energy is rapidly emerging as the primary source of electricity in the Philippines, although developing an accurate wind resource model is difficult. In this study, Weather Research and Forecasting (WRF) Model, an open source mesoscale Numerical Weather Prediction (NWP) model, was used to produce a 1-year atmospheric simulation with 4 km resolution on the Ilocos Region of the Philippines. The WRF output (netCDF) extracts the annual mean wind speed data using a Python-based Graphical User Interface. Lastly, wind resource assessment was produced using a GIS software. Results of the study showed that it is more flexible to use Python scripts than using other post-processing tools in dealing with netCDF files. Using WRF Model, Python, and Geographic Information Systems, a reliable wind resource map is produced. <p class="card-text"><strong>Keywords:</strong> <a href="https://publications.waset.org/abstracts/search?q=wind%20resource%20assessment" title="wind resource assessment">wind resource assessment</a>, <a href="https://publications.waset.org/abstracts/search?q=weather%20research%20and%20forecasting%20%28WRF%29%20model" title=" weather research and forecasting (WRF) model"> weather research and forecasting (WRF) model</a>, <a href="https://publications.waset.org/abstracts/search?q=python" title=" python"> python</a>, <a href="https://publications.waset.org/abstracts/search?q=GIS%20software" title=" GIS software"> GIS software</a> </p> <a href="https://publications.waset.org/abstracts/40795/development-of-a-wind-resource-assessment-framework-using-weather-research-and-forecasting-wrf-model-python-scripting-and-geographic-information-systems" class="btn btn-primary btn-sm">Procedia</a> <a href="https://publications.waset.org/abstracts/40795.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">442</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=forecasting%20model&page=2">2</a></li> <li class="page-item"><a class="page-link" href="https://publications.waset.org/abstracts/search?q=forecasting%20model&page=3">3</a></li> <li class="page-item"><a class="page-link" href="https://publications.waset.org/abstracts/search?q=forecasting%20model&page=4">4</a></li> <li class="page-item"><a class="page-link" href="https://publications.waset.org/abstracts/search?q=forecasting%20model&page=5">5</a></li> <li class="page-item"><a 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