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forecasting</title> <meta name="description" content="Search results for: technology forecasting"> <meta name="keywords" content="technology forecasting"> <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 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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="technology forecasting"> <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> 8183</div> </div> </div> </div> <h1 class="mt-3 mb-3 text-center" style="font-size:1.6rem;">Search results for: technology forecasting</h1> <div class="card paper-listing mb-3 mt-3"> <h5 class="card-header" style="font-size:.9rem"><span class="badge badge-info">8183</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">8182</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">8181</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">8180</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">8179</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">8178</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">8177</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">8176</span> Forecasting Amman Stock Market Data Using a Hybrid Method</h5> <div class="card-body"> <p class="card-text"><strong>Authors:</strong> <a href="https://publications.waset.org/abstracts/search?q=Ahmad%20Awajan">Ahmad Awajan</a>, <a href="https://publications.waset.org/abstracts/search?q=Sadam%20Al%20Wadi"> Sadam Al Wadi</a> </p> <p class="card-text"><strong>Abstract:</strong></p> In this study, a hybrid method based on Empirical Mode Decomposition and Holt-Winter (EMD-HW) is used to forecast Amman stock market data. First, the data are decomposed by EMD method into Intrinsic Mode Functions (IMFs) and residual components. Then, all components are forecasted by HW technique. Finally, forecasting values are aggregated together to get the forecasting value of stock market data. Empirical results showed that the EMD- HW outperform individual forecasting models. The strength of this EMD-HW lies in its ability to forecast non-stationary and non- linear time series without a need to use any transformation method. Moreover, EMD-HW has a relatively high accuracy comparing with eight existing forecasting methods based on the five forecast error measures. <p class="card-text"><strong>Keywords:</strong> <a href="https://publications.waset.org/abstracts/search?q=Holt-Winter%20method" title="Holt-Winter method">Holt-Winter method</a>, <a href="https://publications.waset.org/abstracts/search?q=empirical%20mode%20decomposition" title=" empirical mode decomposition"> empirical mode decomposition</a>, <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> </p> <a href="https://publications.waset.org/abstracts/122857/forecasting-amman-stock-market-data-using-a-hybrid-method" class="btn btn-primary btn-sm">Procedia</a> <a href="https://publications.waset.org/abstracts/122857.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">129</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">8175</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">8174</span> Short Life Cycle Time Series Forecasting</h5> <div class="card-body"> <p class="card-text"><strong>Authors:</strong> <a href="https://publications.waset.org/abstracts/search?q=Shalaka%20Kadam">Shalaka Kadam</a>, <a href="https://publications.waset.org/abstracts/search?q=Dinesh%20Apte"> Dinesh Apte</a>, <a href="https://publications.waset.org/abstracts/search?q=Sagar%20Mainkar"> Sagar Mainkar</a> </p> <p class="card-text"><strong>Abstract:</strong></p> The life cycle of products is becoming shorter and shorter due to increased competition in market, shorter product development time and increased product diversity. Short life cycles are normal in retail industry, style business, entertainment media, and telecom and semiconductor industry. The subject of accurate forecasting for demand of short lifecycle products is of special enthusiasm for many researchers and organizations. Due to short life cycle of products the amount of historical data that is available for forecasting is very minimal or even absent when new or modified products are launched in market. The companies dealing with such products want to increase the accuracy in demand forecasting so that they can utilize the full potential of the market at the same time do not oversupply. This provides the challenge to develop a forecasting model that can forecast accurately while handling large variations in data and consider the complex relationships between various parameters of data. Many statistical models have been proposed in literature for forecasting time series data. Traditional time series forecasting models do not work well for short life cycles due to lack of historical data. Also artificial neural networks (ANN) models are very time consuming to perform forecasting. We have studied the existing models that are used for forecasting and their limitations. This work proposes an effective and powerful forecasting approach for short life cycle time series forecasting. We have proposed an approach which takes into consideration different scenarios related to data availability for short lifecycle products. We then suggest a methodology which combines statistical analysis with structured judgement. Also the defined approach can be applied across domains. We then describe the method of creating a profile from analogous products. This profile can then be used for forecasting products with historical data of analogous products. We have designed an application which combines data, analytics and domain knowledge using point-and-click technology. The forecasting results generated are compared using MAPE, MSE and RMSE error scores. Conclusion: Based on the results it is observed that no one approach is sufficient for short life-cycle forecasting and we need to combine two or more approaches for achieving the desired accuracy. <p class="card-text"><strong>Keywords:</strong> <a href="https://publications.waset.org/abstracts/search?q=forecast" title="forecast">forecast</a>, <a href="https://publications.waset.org/abstracts/search?q=short%20life%20cycle%20product" title=" short life cycle product"> short life cycle product</a>, <a href="https://publications.waset.org/abstracts/search?q=structured%20judgement" title=" structured judgement"> structured judgement</a>, <a href="https://publications.waset.org/abstracts/search?q=time%20series" title=" time series"> time series</a> </p> <a href="https://publications.waset.org/abstracts/33435/short-life-cycle-time-series-forecasting" class="btn btn-primary btn-sm">Procedia</a> <a href="https://publications.waset.org/abstracts/33435.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">358</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">8173</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">8172</span> Technology Futures in Global Militaries: A Forecasting Method Using Abstraction Hierarchies</h5> <div class="card-body"> <p class="card-text"><strong>Authors:</strong> <a href="https://publications.waset.org/abstracts/search?q=Mark%20Andrew">Mark Andrew</a> </p> <p class="card-text"><strong>Abstract:</strong></p> Geopolitical tensions are at a thirty-year high, and the pace of technological innovation is driving asymmetry in force capabilities between nation states and between non-state actors. Technology futures are a vital component of defence capability growth, and investments in technology futures need to be informed by accurate and reliable forecasts of the options for ‘systems of systems’ innovation, development, and deployment. This paper describes a method for forecasting technology futures developed through an analysis of four key systems’ development stages, namely: technology domain categorisation, scanning results examining novel systems’ signals and signs, potential system-of systems’ implications in warfare theatres, and political ramifications in terms of funding and development priorities. The method has been applied to several technology domains, including physical systems (e.g., nano weapons, loitering munitions, inflight charging, and hypersonic missiles), biological systems (e.g., molecular virus weaponry, genetic engineering, brain-computer interfaces, and trans-human augmentation), and information systems (e.g., sensor technologies supporting situation awareness, cyber-driven social attacks, and goal-specification challenges to proliferation and alliance testing). Although the current application of the method has been team-centred using paper-based rapid prototyping and iteration, the application of autonomous language models (such as GPT-3) is anticipated as a next-stage operating platform. The importance of forecasting accuracy and reliability is considered a vital element in guiding technology development to afford stronger contingencies as ideological changes are forecast to expand threats to ecology and earth systems, possibly eclipsing the traditional vulnerabilities of nation states. The early results from the method will be subjected to ground truthing using longitudinal investigation. <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=technology%20futures" title=" technology futures"> technology futures</a>, <a href="https://publications.waset.org/abstracts/search?q=uncertainty" title=" uncertainty"> uncertainty</a>, <a href="https://publications.waset.org/abstracts/search?q=complexity" title=" complexity"> complexity</a> </p> <a href="https://publications.waset.org/abstracts/155593/technology-futures-in-global-militaries-a-forecasting-method-using-abstraction-hierarchies" class="btn btn-primary btn-sm">Procedia</a> <a href="https://publications.waset.org/abstracts/155593.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">114</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">8171</span> pscmsForecasting: A Python Web Service for Time Series Forecasting</h5> <div class="card-body"> <p class="card-text"><strong>Authors:</strong> <a href="https://publications.waset.org/abstracts/search?q=Ioannis%20Andrianakis">Ioannis Andrianakis</a>, <a href="https://publications.waset.org/abstracts/search?q=Vasileios%20Gkatas"> Vasileios Gkatas</a>, <a href="https://publications.waset.org/abstracts/search?q=Nikos%20Eleftheriadis"> Nikos Eleftheriadis</a>, <a href="https://publications.waset.org/abstracts/search?q=Alexios%20Ellinidis"> Alexios Ellinidis</a>, <a href="https://publications.waset.org/abstracts/search?q=Ermioni%20Avramidou"> Ermioni Avramidou</a> </p> <p class="card-text"><strong>Abstract:</strong></p> pscmsForecasting is an open-source web service that implements a variety of time series forecasting algorithms and exposes them to the user via the ubiquitous HTTP protocol. It allows developers to enhance their applications by adding time series forecasting functionalities through an intuitive and easy-to-use interface. This paper provides some background on time series forecasting and gives details about the implemented algorithms, aiming to enhance the end user’s understanding of the underlying methods before incorporating them into their applications. A detailed description of the web service’s interface and its various parameterizations is also provided. Being an open-source project, pcsmsForecasting can also be easily modified and tailored to the specific needs of each application. <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=forecasting" title=" forecasting"> forecasting</a>, <a href="https://publications.waset.org/abstracts/search?q=web%20service" title=" web service"> web service</a>, <a href="https://publications.waset.org/abstracts/search?q=open%20source" title=" open source"> open source</a> </p> <a href="https://publications.waset.org/abstracts/170621/pscmsforecasting-a-python-web-service-for-time-series-forecasting" class="btn btn-primary btn-sm">Procedia</a> <a href="https://publications.waset.org/abstracts/170621.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">83</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">8170</span> Comparing Forecasting Performances of the Bass Diffusion Model and Time Series Methods for Sales of Electric Vehicles</h5> <div class="card-body"> <p class="card-text"><strong>Authors:</strong> <a href="https://publications.waset.org/abstracts/search?q=Andreas%20Gohs">Andreas Gohs</a>, <a href="https://publications.waset.org/abstracts/search?q=Reinhold%20Kosfeld"> Reinhold Kosfeld</a> </p> <p class="card-text"><strong>Abstract:</strong></p> This study should be of interest for practitioners who want to predict precisely the sales numbers of vehicles equipped with an innovative propulsion technology as well as for researchers interested in applied (regional) time series analysis. The study is based on the numbers of new registrations of pure electric and hybrid cars. Methods of time series analysis like ARIMA are compared with the Bass Diffusion-model concerning their forecasting performances for new registrations in Germany at the national and federal state levels. Especially it is investigated if the additional information content from regional data increases the forecasting accuracy for the national level by adding predictions for the federal states. Results of parameters of the Bass Diffusion Model estimated for Germany and its sixteen federal states are reported. While the focus of this research is on the German market, estimation results are also provided for selected European and other countries. Concerning Bass-parameters and forecasting performances, we get very different results for Germany's federal states and the member states of the European Union. This corresponds to differences across the EU-member states in the adoption process of this innovative technology. Concerning the German market, the adoption is rather proceeded in southern Germany and stays behind in Eastern Germany except for Berlin. <p class="card-text"><strong>Keywords:</strong> <a href="https://publications.waset.org/abstracts/search?q=bass%20diffusion%20model" title="bass diffusion model">bass diffusion model</a>, <a href="https://publications.waset.org/abstracts/search?q=electric%20vehicles" title=" electric vehicles"> electric vehicles</a>, <a href="https://publications.waset.org/abstracts/search?q=forecasting%20performance" title=" forecasting performance"> forecasting performance</a>, <a href="https://publications.waset.org/abstracts/search?q=market%20diffusion" title=" market diffusion"> market diffusion</a> </p> <a href="https://publications.waset.org/abstracts/118712/comparing-forecasting-performances-of-the-bass-diffusion-model-and-time-series-methods-for-sales-of-electric-vehicles" class="btn btn-primary btn-sm">Procedia</a> <a href="https://publications.waset.org/abstracts/118712.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">166</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">8169</span> Utilizing Grid Computing to Enhance Power Systems Performance</h5> <div class="card-body"> <p class="card-text"><strong>Authors:</strong> <a href="https://publications.waset.org/abstracts/search?q=Rafid%20A.%20Al-Khannak">Rafid A. Al-Khannak</a>, <a href="https://publications.waset.org/abstracts/search?q=Fawzi%20M.%20Al-Naima"> Fawzi M. Al-Naima</a> </p> <p class="card-text"><strong>Abstract:</strong></p> Power load is one of the most important controlling keys which decide power demands and illustrate power usage to shape power market. Hence, power load forecasting is the parameter which facilitates understanding and analyzing all these aspects. In this paper, power load forecasting is solved under MATLAB environment by constructing a neural network for the power load to find an accurate simulated solution with the minimum error. A developed algorithm to achieve load forecasting application with faster technique is the aim for this paper. The algorithm is used to enable MATLAB power application to be implemented by multi machines in the Grid computing system, and to accomplish it within much less time, cost and with high accuracy and quality. Grid Computing, the modern computational distributing technology, has been used to enhance the performance of power applications by utilizing idle and desired Grid contributor(s) by sharing computational power resources. <p class="card-text"><strong>Keywords:</strong> <a href="https://publications.waset.org/abstracts/search?q=DeskGrid" title="DeskGrid">DeskGrid</a>, <a href="https://publications.waset.org/abstracts/search?q=Grid%20Server" title=" Grid Server"> Grid Server</a>, <a href="https://publications.waset.org/abstracts/search?q=idle%20contributor%28s%29" title=" idle contributor(s)"> idle contributor(s)</a>, <a href="https://publications.waset.org/abstracts/search?q=grid%20computing" title=" grid computing"> grid computing</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/7716/utilizing-grid-computing-to-enhance-power-systems-performance" class="btn btn-primary btn-sm">Procedia</a> <a href="https://publications.waset.org/abstracts/7716.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">475</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">8168</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">8167</span> Two Day Ahead Short Term Load Forecasting Neural Network Based</h5> <div class="card-body"> <p class="card-text"><strong>Authors:</strong> <a href="https://publications.waset.org/abstracts/search?q=Firas%20M.%20Tuaimah">Firas M. Tuaimah</a> </p> <p class="card-text"><strong>Abstract:</strong></p> This paper presents an Artificial Neural Network based approach for short-term load forecasting and exactly for two days ahead. Two seasons have been discussed for Iraqi power system, namely summer and winter; the hourly load demand is the most important input variables for ANN based load forecasting. The recorded daily load profile with a lead time of 1-48 hours for July and December of the year 2012 was obtained from the operation and control center that belongs to the Ministry of Iraqi electricity. The results of the comparison show that the neural network gives a good prediction for the load forecasting and for two days ahead. <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=artificial%20neural%20networks" title=" artificial neural networks"> artificial neural networks</a>, <a href="https://publications.waset.org/abstracts/search?q=back%20propagation%20learning" title=" back propagation learning"> back propagation learning</a>, <a href="https://publications.waset.org/abstracts/search?q=hourly%20load%20demand" title=" hourly load demand"> hourly load demand</a> </p> <a href="https://publications.waset.org/abstracts/7878/two-day-ahead-short-term-load-forecasting-neural-network-based" class="btn btn-primary btn-sm">Procedia</a> <a href="https://publications.waset.org/abstracts/7878.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">464</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">8166</span> Forecasting Future Demand for Energy Efficient Vehicles: A Review of Methodological Approaches</h5> <div class="card-body"> <p class="card-text"><strong>Authors:</strong> <a href="https://publications.waset.org/abstracts/search?q=Dimitrios%20I.%20Tselentis">Dimitrios I. Tselentis</a>, <a href="https://publications.waset.org/abstracts/search?q=Simon%20P.%20Washington"> Simon P. Washington</a> </p> <p class="card-text"><strong>Abstract:</strong></p> Considerable literature has been focused over the last few decades on forecasting the consumer demand of Energy Efficient Vehicles (EEVs). These methodological issues range from how to capture recent purchase decisions in revealed choice studies and how to set up experiments in stated preference (SP) studies, and choice of analysis method for analyzing such data. This paper reviews the plethora of published studies on the field of forecasting demand of EEVs since 1980, and provides a review and annotated bibliography of that literature as it pertains to this particular demand forecasting problem. This detailed review addresses the literature not only to Transportation studies, but specifically to the problem and methodologies around forecasting to the time horizons of planning studies which may represent 10 to 20 year forecasts. The objectives of the paper are to identify where existing gaps in literature exist and to articulate where promising methodologies might guide longer term forecasting. One of the key findings of this review is that there are many common techniques used both in the field of new product demand forecasting and the field of predicting future demand for EEV. Apart from SP and RP methods, some of these new techniques that have emerged in the literature in the last few decades are survey related approaches, product diffusion models, time-series modelling, computational intelligence models and other holistic approaches. <p class="card-text"><strong>Keywords:</strong> <a href="https://publications.waset.org/abstracts/search?q=demand%20forecasting" title="demand forecasting">demand forecasting</a>, <a href="https://publications.waset.org/abstracts/search?q=Energy%20Efficient%20Vehicles%20%28EEVs%29" title=" Energy Efficient Vehicles (EEVs)"> Energy Efficient Vehicles (EEVs)</a>, <a href="https://publications.waset.org/abstracts/search?q=forecasting%20methodologies%20review" title=" forecasting methodologies review"> forecasting methodologies review</a>, <a href="https://publications.waset.org/abstracts/search?q=methodological%20approaches" title=" methodological approaches"> methodological approaches</a> </p> <a href="https://publications.waset.org/abstracts/15014/forecasting-future-demand-for-energy-efficient-vehicles-a-review-of-methodological-approaches" class="btn btn-primary btn-sm">Procedia</a> <a href="https://publications.waset.org/abstracts/15014.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">489</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">8165</span> Forecasting of Grape Juice Flavor by Using Support Vector Regression</h5> <div class="card-body"> <p class="card-text"><strong>Authors:</strong> <a href="https://publications.waset.org/abstracts/search?q=Ren-Jieh%20Kuo">Ren-Jieh Kuo</a>, <a href="https://publications.waset.org/abstracts/search?q=Chun-Shou%20Huang"> Chun-Shou Huang</a> </p> <p class="card-text"><strong>Abstract:</strong></p> The research of juice flavor forecasting has become more important in China. Due to the fast economic growth in China, many different kinds of juices have been introduced to the market. If a beverage company can understand their customers’ preference well, the juice can be served more attractively. Thus, this study intends to introduce the basic theory and computing process of grapes juice flavor forecasting based on support vector regression (SVR). Applying SVR, BPN and LR to forecast the flavor of grapes juice in real data, the result shows that SVR is more suitable and effective at predicting performance. <p class="card-text"><strong>Keywords:</strong> <a href="https://publications.waset.org/abstracts/search?q=flavor%20forecasting" title="flavor forecasting">flavor forecasting</a>, <a href="https://publications.waset.org/abstracts/search?q=artificial%20neural%20networks" title=" artificial neural networks"> artificial neural networks</a>, <a href="https://publications.waset.org/abstracts/search?q=Support%20Vector%20Regression" title=" Support Vector Regression"> Support Vector Regression</a>, <a href="https://publications.waset.org/abstracts/search?q=China" title=" China"> China</a> </p> <a href="https://publications.waset.org/abstracts/21311/forecasting-of-grape-juice-flavor-by-using-support-vector-regression" class="btn btn-primary btn-sm">Procedia</a> <a href="https://publications.waset.org/abstracts/21311.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">492</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">8164</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">8163</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">8162</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">8161</span> Electric Load Forecasting Based on Artificial Neural Network for Iraqi Power System</h5> <div class="card-body"> <p class="card-text"><strong>Authors:</strong> <a href="https://publications.waset.org/abstracts/search?q=Afaneen%20Anwer">Afaneen Anwer</a>, <a href="https://publications.waset.org/abstracts/search?q=Samara%20M.%20Kamil"> Samara M. Kamil</a> </p> <p class="card-text"><strong>Abstract:</strong></p> Load Forecast required prediction accuracy based on optimal operation and maintenance. A good accuracy is the basis of economic dispatch, unit commitment, and system reliability. A good load forecasting system fulfilled fast speed, automatic bad data detection, and ability to access the system automatically to get the needed data. In this paper, the formulation of the load forecasting is discussed and the solution is obtained by using artificial neural network method. A MATLAB environment has been used to solve the load forecasting schedule of Iraqi super grid network considering the daily load for three years. The obtained results showed a good accuracy in predicting the forecasted load. <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=neural%20network" title=" neural network"> neural network</a>, <a href="https://publications.waset.org/abstracts/search?q=back-propagation%20algorithm" title=" back-propagation algorithm"> back-propagation algorithm</a>, <a href="https://publications.waset.org/abstracts/search?q=Iraqi%20power%20system" title=" Iraqi power system"> Iraqi power system</a> </p> <a href="https://publications.waset.org/abstracts/19308/electric-load-forecasting-based-on-artificial-neural-network-for-iraqi-power-system" class="btn btn-primary btn-sm">Procedia</a> <a href="https://publications.waset.org/abstracts/19308.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">583</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">8160</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">8159</span> Electricity Price Forecasting: A Comparative Analysis with Shallow-ANN and DNN</h5> <div class="card-body"> <p class="card-text"><strong>Authors:</strong> <a href="https://publications.waset.org/abstracts/search?q=Faz%C4%B1l%20G%C3%B6kg%C3%B6z">Fazıl Gökgöz</a>, <a href="https://publications.waset.org/abstracts/search?q=Fahrettin%20Filiz"> Fahrettin Filiz</a> </p> <p class="card-text"><strong>Abstract:</strong></p> Electricity prices have sophisticated features such as high volatility, nonlinearity and high frequency that make forecasting quite difficult. Electricity price has a volatile and non-random character so that, it is possible to identify the patterns based on the historical data. Intelligent decision-making requires accurate price forecasting for market traders, retailers, and generation companies. So far, many shallow-ANN (artificial neural networks) models have been published in the literature and showed adequate forecasting results. During the last years, neural networks with many hidden layers, which are referred to as DNN (deep neural networks) have been using in the machine learning community. The goal of this study is to investigate electricity price forecasting performance of the shallow-ANN and DNN models for the Turkish day-ahead electricity market. The forecasting accuracy of the models has been evaluated with publicly available data from the Turkish day-ahead electricity market. Both shallow-ANN and DNN approach would give successful result in forecasting problems. Historical load, price and weather temperature data are used as the input variables for the models. The data set includes power consumption measurements gathered between January 2016 and December 2017 with one-hour resolution. In this regard, forecasting studies have been carried out comparatively with shallow-ANN and DNN models for Turkish electricity markets in the related time period. The main contribution of this study is the investigation of different shallow-ANN and DNN models in the field of electricity price forecast. All models are compared regarding their MAE (Mean Absolute Error) and MSE (Mean Square) results. DNN models give better forecasting performance compare to shallow-ANN. Best five MAE results for DNN models are 0.346, 0.372, 0.392, 0,402 and 0.409. <p class="card-text"><strong>Keywords:</strong> <a href="https://publications.waset.org/abstracts/search?q=deep%20learning" title="deep learning">deep learning</a>, <a href="https://publications.waset.org/abstracts/search?q=artificial%20neural%20networks" title=" artificial neural networks"> artificial neural networks</a>, <a href="https://publications.waset.org/abstracts/search?q=energy%20price%20forecasting" title=" energy price forecasting"> energy price forecasting</a>, <a href="https://publications.waset.org/abstracts/search?q=turkey" title=" turkey"> turkey</a> </p> <a href="https://publications.waset.org/abstracts/91064/electricity-price-forecasting-a-comparative-analysis-with-shallow-ann-and-dnn" class="btn btn-primary btn-sm">Procedia</a> <a href="https://publications.waset.org/abstracts/91064.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">292</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">8158</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">8157</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">8156</span> Energy Consumption Forecast Procedure for an Industrial Facility</h5> <div class="card-body"> <p class="card-text"><strong>Authors:</strong> <a href="https://publications.waset.org/abstracts/search?q=Tatyana%20Aleksandrovna%20Barbasova">Tatyana Aleksandrovna Barbasova</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=Olga%20Valerevna%20Kolesnikova"> Olga Valerevna Kolesnikova</a>, <a href="https://publications.waset.org/abstracts/search?q=Aleksandra%20Aleksandrovna%20Filimonova"> Aleksandra Aleksandrovna Filimonova</a> </p> <p class="card-text"><strong>Abstract:</strong></p> We regard forecasting of energy consumption by private production areas of a large industrial facility as well as by the facility itself. As for production areas the forecast is made based on empirical dependencies of the specific energy consumption and the production output. As for the facility itself implementation of the task to minimize the energy consumption forecasting error is based on adjustment of the facility’s actual energy consumption values evaluated with the metering device and the total design energy consumption of separate production areas of the facility. The suggested procedure of optimal energy consumption was tested based on the actual data of core product output and energy consumption by a group of workshops and power plants of the large iron and steel facility. Test results show that implementation of this procedure gives the mean accuracy of energy consumption forecasting for winter 2014 of 0.11% for the group of workshops and 0.137% for the power plants. <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%20consumption%20forecasting%20error" title=" energy consumption forecasting error"> energy consumption forecasting error</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=forecasting%20accuracy" title=" forecasting accuracy"> forecasting accuracy</a>, <a href="https://publications.waset.org/abstracts/search?q=forecasting" title=" forecasting"> forecasting</a> </p> <a href="https://publications.waset.org/abstracts/38729/energy-consumption-forecast-procedure-for-an-industrial-facility" class="btn btn-primary btn-sm">Procedia</a> <a href="https://publications.waset.org/abstracts/38729.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">445</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">8155</span> Estimation and Forecasting Debris Flow Phenomena on the Highway of the 'TRACECA' Corridor</h5> <div class="card-body"> <p class="card-text"><strong>Authors:</strong> <a href="https://publications.waset.org/abstracts/search?q=Levan%20Tsulukidze">Levan Tsulukidze</a> </p> <p class="card-text"><strong>Abstract:</strong></p> The paper considers debris flow phenomena and forecasting of them in the corridor of ‘TRACECA’ on the example of river Naokhrevistkali, as well as the debris flow -type channel passing between the villages of Vale-2 and Naokhrevi. As a result of expeditionary and reconnaissance investigations, as well as using empiric dependencies, the debris flow expenditure has been estimated in case of different debris flow provisions. <p class="card-text"><strong>Keywords:</strong> <a href="https://publications.waset.org/abstracts/search?q=debris%20flow" title="debris flow">debris flow</a>, <a href="https://publications.waset.org/abstracts/search?q=Traceca%20corridor" title=" Traceca corridor"> Traceca corridor</a>, <a href="https://publications.waset.org/abstracts/search?q=forecasting" title=" forecasting"> forecasting</a>, <a href="https://publications.waset.org/abstracts/search?q=river%20Naokhrevistkali" title=" river Naokhrevistkali"> river Naokhrevistkali</a> </p> <a href="https://publications.waset.org/abstracts/47669/estimation-and-forecasting-debris-flow-phenomena-on-the-highway-of-the-traceca-corridor" class="btn btn-primary btn-sm">Procedia</a> <a href="https://publications.waset.org/abstracts/47669.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">353</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">8154</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> <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=technology%20forecasting&page=2">2</a></li> <li class="page-item"><a class="page-link" href="https://publications.waset.org/abstracts/search?q=technology%20forecasting&page=3">3</a></li> <li class="page-item"><a class="page-link" href="https://publications.waset.org/abstracts/search?q=technology%20forecasting&page=4">4</a></li> <li class="page-item"><a class="page-link" href="https://publications.waset.org/abstracts/search?q=technology%20forecasting&page=5">5</a></li> <li class="page-item"><a class="page-link" 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