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Decomposition of time-series by level and change

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  • Tessier, Thomas H.
  • Armstrong, J. Scott

Abstract

This article examines whether decomposing time series data into two parts – level and change – produces forecasts that are more accurate than those from forecasting the aggregate directly. Prior research found that, in general, decomposition reduced forecasting errors by 35%. An earlier study on decomposition into level and change found a forecast error reduction of 23%. The current study found that nowcasts consisting of a simple average of estimates from preliminary surveys and econometric models of the U.S. lodging market, improved the accuracy of final estimates of levels. Forecasts of change from an econometric model and the improved nowcasts reduced forecast errors by 29% when compared to direct forecasts of the aggregate. Forecasts of change from an extrapolation model and the improved nowcasts reduced forecast errors by 45%. On average then, the error reduction for this study was 37%.

Suggested Citation

  • Tessier, Thomas H. & Armstrong, J. Scott, 2015. "Decomposition of time-series by level and change," Journal of Business Research, Elsevier, vol. 68(8), pages 1755-1758.
    • Handle: RePEc:eee:jbrese:v:68:y:2015:i:8:p:1755-1758
    DOI: 10.1016/j.jbusres.2015.03.035
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    1. Armstrong, J. Scott & Green, Kesten C. & Graefe, Andreas, 2015. "Golden rule of forecasting: Be conservative," Journal of Business Research, Elsevier, vol. 68(8), pages 1717-1731.
    2. Armstrong, J. Scott, 1970. "An Application of Econometric Models to International Marketing," MPRA Paper 81698, University Library of Munich, Germany.
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    6. J. Scott Armstrong & Michael C. Grohman, 1972. "A Comparative Study of Methods for Long-Range Market Forecasting," Management Science, INFORMS, vol. 19(2), pages 211-221, October.
    7. David E. Runkle, 1998. "Revisionist history: how data revisions distort economic policy research," Quarterly Review, Federal Reserve Bank of Minneapolis, vol. 22(Fall), pages 3-12.
    8. Graefe, Andreas, 2015. "Improving forecasts using equally weighted predictors," Journal of Business Research, Elsevier, vol. 68(8), pages 1792-1799.
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    1. Armstrong, J. Scott & Green, Kesten C. & Graefe, Andreas, 2015. "Golden rule of forecasting: Be conservative," Journal of Business Research, Elsevier, vol. 68(8), pages 1717-1731.
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