The Korean Journal of Applied Statistics (응용통계연구)

The Korean Statistical Society (한국통계학회)
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Long-Term Forecasting by Wavelet-Based Filter Bank Selections and Its Application

  • Lee, Jeong-Ran (Department of Statistics, Seoul National University) ;
  • Lee, You-Lim (Cooperative Banking Support Department, NH Bank) ;
  • Oh, Hee-Seok (Department of Statistics, Seoul National University)
  • Received : 20100100
  • Accepted : 20100300
  • Published : 2010.04.30
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Abstract

Long-term forecasting of seasonal time series is critical in many applications such as planning business strategies and resolving possible problems of a business company. Unlike the traditional approach that depends solely on dynamic models, Li and Hinich (2002) introduced a combination of stochastic dynamic modeling with filter bank approach for forecasting seasonal patterns using highly coherent(High-C) waveforms. We modify the filter selection and forecasting procedure on wavelet domain to be more feasible and compare the resulting predictor with one that obtained from the wavelet variance estimation method. An improvement over other seasonal pattern extraction and forecasting methods based on such as wavelet scalogram, Holt-Winters, and seasonal autoregressive integrated moving average(SARIMA) is shown in terms of the prediction error. The performance of the proposed method is illustrated by a simulation study and an application to the real stock price data.

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References

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