Hybrid multi-model forecasting system: A case study on display market

Chen-Chun Lin, Chun-Ling Lin, Joseph Z. Shyu

Research output: Contribution to journalArticle

6 Scopus citations

Abstract

This paper provides a novel hybrid multi-model forecasting system, with a special focus on the changing regional market demand in the display markets. Through an intensive case study of the ups and downs of the display industry, this paper examines the panel makers suffered from low panel price and unstable market demand, then they have changed to react to the rapid demand in the market or have lower panel stock for keeping supply and demand more balanced. In addition, this paper suggests a co-evolution forecasting process of sales and market factor. It can automatically apply various combinations of both linear and nonlinear models, and which alternatives deliver the lowest statistical error and produce a good estimate for the prediction of markets.

Moreover, this article shows how the system is modeled and its accuracy is proved by means of experimental results; and judged by 3 evaluation criteria, including the mean square error (MSE), the mean absolute percentage error (MAPE), and the average square root error (ASRE) were used as the performance criteria to automatically select the optimal forecasting model. Finally, the results showed that the proposed system had considerably better predictive performance than previous and individual models. To summarize, the proposed system can reduce the user's effort for easier obtaining the desired forecasting results and create high quality forecasts. (C) 2014 Elsevier B.V. All rights reserved.
Original languageEnglish
Pages (from-to)279-289
Number of pages11
JournalKnowledge-Based Systems
Volume71
DOIs
StatePublished - Nov 2014

Keywords

  • NONLINEAR FORECASTS; NEURAL NETWORKS; MODELS; SUPPORT; ARIMA
  • Hybrid multi-model forecasting system; Prediction; Display markets; Mean square error (MSE); Mean absolute percentage error (MAPE); Average square root error (ASRE)

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