The multivariate moving window spectral method

  • Authors:
  • Dennis Ridley;Pierre Ngnepieba

  • Affiliations:
  • SBI, Florida A & M University and School of Computational Science, FSU, Tallahassee, FL 32306-4120, USA;Department of Mathematics, Florida A & M University Tallahassee, FL 32307, USA

  • Venue:
  • Computers and Industrial Engineering
  • Year:
  • 2009

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Abstract

The Univariate Moving Window Spectral method is extended to the Multivariate Moving Window Spectral method (MWS). This further mitigates the bias encountered in time series model parameter estimates, that otherwise results in multiple-period lead time forecast error divergence. Compared to time domain methods, the spectral approach provides for better estimation of cyclical components in time series. When recombined by the MWS paradigm, better long range forecasts are possible. The method is illustrated by a tri-variate sales, price and income case study. The multivariate MWS method requires little user expertise, explains the data better, and forecasts better. It is applicable to a broad range of biomedical, physical, economic, and social science time series.