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Integrated application of time series multiple-interventions analysis and knowledge-based reasoning

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  • Yuehjen Shao
  • Yue-Fa Lin
  • Soe-Tsyr Yuan

Abstract

This study examines the data that result from multiple promotional strategies when the data are autocorrelated. Time series intervention analysis is the traditional way to analyze such data, focusing on the effects of a single or a few interventions. Time series intervention analysis delivers good results, provided that there is a known and predetermined schedule of future interventions. This study opts for a different type of analysis. Instead of adopting the traditional time series intervention analysis with only one or a few interventions, this study explores the possibility of integrating time series intervention analysis and a knowledge-based system to analyze multiple-interventions data. This integrated approach does not require attempts to ascertain the effects of future interventions. Through the analysis of actual promotion data, this study shows the benefits of using the proposed method.

Suggested Citation

  • Yuehjen Shao & Yue-Fa Lin & Soe-Tsyr Yuan, 1999. "Integrated application of time series multiple-interventions analysis and knowledge-based reasoning," Journal of Applied Statistics, Taylor & Francis Journals, vol. 26(6), pages 755-766.
    • Handle: RePEc:taf:japsta:v:26:y:1999:i:6:p:755-766
    DOI: 10.1080/02664769922197
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