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

• Main Authors: Seyedi, Seyed Navid, Rezvan, Pouyan, Akbarnatajbisheh, Saeed, Syed Hassan, Syed Ahmad Helmi
• Format: Article
• Language: English
• Published: Trans Tech Publications, Switzerland 2014
• Subjects: TJ Mechanical engineering and machinery
• Online Access: 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 Neural Network (ANN), and ARIMA-ANN hybrid model are well-known forecasting models. Many researchers concluded that the Hybrid model is the predominant forecasting model in comparison with ARIMA and ANN individual models. Most of these researches are based on non-stationary or seasonal timeseries, whereas in this article, hybrid models forecast ability by stationary time series is studied. Some following demand time steps from a paint manufacturing company are forecasted by all previously mentioned models and ARIMA-ANN hybrid model fails to present the best forecasts.