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Revenue characterisation with Singular Spectrum Analysis

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  • Rajiv Sambasivan

Abstract

In this work, a method to characterise the daily sales revenue for an online store is presented. Daily sales revenue is a time series. The developed characterisation identifies the major sources of variation in the time series. Such a characterisation can be used for purposes such as developing structural forecasting models and extracting insights that can be leveraged for business and operations planning. In this work, this characterisation is developed using a technique called Singular Spectrum Analysis. Achieving good results with Singular Spectrum Analysis requires the judicious selection of an algorithm parameter called the window length. A framework to select this parameter is provided. Literature survey revealed that applications of Singular Spectrum Analysis to business data are limited. To the best of found knowledge from the literature survey, Singular Spectrum Analysis has not been applied to retail revenue stream analysis.

Suggested Citation

  • Rajiv Sambasivan, 2021. "Revenue characterisation with Singular Spectrum Analysis," Journal of Business Analytics, Taylor & Francis Journals, vol. 4(2), pages 140-154, July.
    • Handle: RePEc:taf:tjbaxx:v:4:y:2021:i:2:p:140-154
    DOI: 10.1080/2573234X.2021.1970483
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