Evaluating ARIMA-neural network hybrid model performance in forecasting stationary timeseries

• 主要な著者: Seyedi, Seyed Navid, Rezvan, Pouyan, Akbarnatajbisheh, Saeed, Syed Hassan, Syed Ahmad Helmi
• フォーマット: 論文
• 言語: English
• 出版事項: Trans Tech Publications, Switzerland 2014
• 主題: TJ Mechanical engineering and machinery
• オンライン･アクセス: http://eprints.utm.my/id/eprint/52746/1/SyedAhmadHelmi2014_EvaluatingARIMA-neuralnetwork.pdf; http://eprints.utm.my/id/eprint/52746/; https://dx.doi.org/10.4028/www.scientific.net/AMR.845.510

Demand prediction is one of most sophisticated steps in planning and investments. Although many studies are conducted to find the appropriate forecasting models, dynamic nature of forecasted parameters and their effecting factors are apparent evidences for continuous researches. ARIMA, Artificial Ne...