Revenue forecasting using a least-squares support vector regression model in a fuzzy environment

  • Authors:

  • Kuo-Ping Lin;Ping-Feng Pai;Yu-Ming Lu;Ping-Teng Chang

  • Affiliations:

  • Department of Information Management, Lunghwa University of Science and Technology, Taoyuan 333, Taiwan;Department of Information Management, National Chi Nan University, 1 University Rd., Puli, Nantou 545, Taiwan;Department of Information Management, Lunghwa University of Science and Technology, Taoyuan 333, Taiwan;Department of Industrial Engineering and Enterprise Information, Box 985, Tunghai University, Taichung 407, Taiwan

  • Venue:
  • Information Sciences: an International Journal
  • Year:
  • 2013

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Abstract

Revenue forecasting is difficult but essential for companies that want to create high-quality revenue budgets, especially in an uncertain economic environment with changing government policies. Under these conditions, the subjective judgment of decision makers is a crucial factor in making accurate forecasts. This investigation develops a fuzzy least-squares support vector regression model with genetic algorithms (FLSSVRGA) to forecast seasonal revenues. The FLSSVRGA uses the H-level to control the possibility distribution range yielded by the fuzzy model and to provide the fuzzy prediction interval. Depending on various factors, such as the global economy and government policies, a decision maker can elect a different level for H using the FLSSVRGA. The proposed FLSSVRGA model is a rolling forecasting model with time series data updated monthly that predicts revenue for the coming month. Four other forecasting models: the seasonal autoregressive integrated moving average (SARIMA), generalized regression neural networks (GRNN), support vector regression with genetic algorithms (SVRGA) and least-squares support vector regression with genetic algorithms (LSSVRGA), are employed to forecast the same data sets. The experimental results indicate that the FLSSVRGA model outperforms all four models in terms of forecasting accuracy. Thus, the FLSSVRGA model is a useful alternative for forecasting seasonal time series data in an uncertain environment; it can provide a user-defined fuzzy prediction interval for decision makers.