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Forecastability: Insights from Physics, Graphical Decomposition, and Information Theory

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  • Peter M. Catt

Abstract

Catt’s aim with this paper is to equip forecasters with some cross-disciplinary theory on forecastability and to provide practical techniques for assessing how forecastable a historical time series is. A time series is a sequence of values at equally spaced time intervals: days, weeks, months, quarters, or years. The historical time series can be viewed as an outcome (realization) of an underlying data generating process (DGP). Assessments of forecastability require an understanding of the DGP and its components. Copyright International Institute of Forecasters, 2009

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  • Peter M. Catt, 2009. "Forecastability: Insights from Physics, Graphical Decomposition, and Information Theory," Foresight: The International Journal of Applied Forecasting, International Institute of Forecasters, issue 13, pages 24-33, Spring.
    • Handle: RePEc:for:ijafaa:y:2009:1:13:p:24-33
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    Cited by:

    1. Hill, Arthur V. & Zhang, Weiyong & Burch, Gerald F., 2015. "Forecasting the forecastability quotient for inventory management," International Journal of Forecasting, Elsevier, vol. 31(3), pages 651-663.
    2. Jiří Šindelář, 2019. "Sales forecasting in financial distribution: a comparison of quantitative forecasting methods," Journal of Financial Services Marketing, Palgrave Macmillan, vol. 24(3), pages 69-80, December.

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