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Enhancing business cycle analysis by integrating anomaly detection and components decomposition of time series data

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  • Koki Kyo

    (Gifu Shotoku Gakuen University)

Abstract

This study presents an innovative approach for detecting and estimating outliers in time series data, emphasizing constrained-remaining components decomposition. The method extends the moving linear model to accommodate outliers, resulting in an enhanced moving linear model. A state-space representation improves computational efficiency through Bayesian estimation. We introduce a novel method for determining outlier positions, starting with initial estimates of the remaining components. The proposed methodology combines maximum likelihood and Bayesian-type estimation for effective outlier detection and estimation, guided by the minimum Akaike Information Criterion (AIC). Furthermore, we investigate outlier detection in time series data with seasonal components. Applications to real data, specifically the Index of Industrial Production (IIP) and Wholesale Commercial Sales (WCS) in Japan, showcase the simplicity and potential for automation in the proposed approach, making it a promising tool for time series analysis, particularly in constrained-remaining components decomposition.

Suggested Citation

  • Koki Kyo, 2025. "Enhancing business cycle analysis by integrating anomaly detection and components decomposition of time series data," Statistical Methods & Applications, Springer;Società Italiana di Statistica, vol. 34(1), pages 129-154, March.
    • Handle: RePEc:spr:stmapp:v:34:y:2025:i:1:d:10.1007_s10260-025-00780-6
    DOI: 10.1007/s10260-025-00780-6
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    References listed on IDEAS

    as
    1. Satoshi Urasawa, 2018. "Structural Change and Business Cycles in Japan: Revisiting the Stylized Facts," Journal of Business Cycle Research, Springer;Centre for International Research on Economic Tendency Surveys (CIRET), vol. 14(2), pages 243-281, November.
    2. Yoshihiro Ohtsuka, 2018. "Large Shocks and the Business Cycle: The Effect of Outlier Adjustments," Journal of Business Cycle Research, Springer;Centre for International Research on Economic Tendency Surveys (CIRET), vol. 14(1), pages 143-178, April.
    3. Koki Kyo & Hideo Noda & Genshiro Kitagawa, 2022. "Co-movement of Cyclical Components Approach to Construct a Coincident Index of Business Cycles," Journal of Business Cycle Research, Springer;Centre for International Research on Economic Tendency Surveys (CIRET), vol. 18(1), pages 101-127, March.
    4. Koki Kyo & Hideo Noda & Fengqi Fang, 2024. "An integrated approach for decomposing time series data into trend, cycle and seasonal components," Mathematical and Computer Modelling of Dynamical Systems, Taylor & Francis Journals, vol. 30(1), pages 792-813, December.
    5. Koki Kyo & Genshiro Kitagawa, 2023. "A Moving Linear Model Approach for Extracting Cyclical Variation from Time Series Data," Journal of Business Cycle Research, Springer;Centre for International Research on Economic Tendency Surveys (CIRET), vol. 19(3), pages 373-397, November.
    Full references (including those not matched with items on IDEAS)

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    More about this item

    Keywords

    Anomaly detection; Economic time series; Cyclical variation; Moving linear model approach; Seasonal adjustment;
    All these keywords.

    JEL classification:

    • C11 - Mathematical and Quantitative Methods - - Econometric and Statistical Methods and Methodology: General - - - Bayesian Analysis: General
    • C14 - Mathematical and Quantitative Methods - - Econometric and Statistical Methods and Methodology: General - - - Semiparametric and Nonparametric Methods: General
    • C18 - Mathematical and Quantitative Methods - - Econometric and Statistical Methods and Methodology: General - - - Methodolical Issues: General
    • E32 - Macroeconomics and Monetary Economics - - Prices, Business Fluctuations, and Cycles - - - Business Fluctuations; Cycles

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