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A State Space Modeling Approach for Time Series Forecasting

Author

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  • Tep Sastri

    (Department of Industrial Engineering, Texas A&M University, College Station, Texas 77843)

Abstract

A stochastic filtering method is presented for on-line recursive estimation and forecasting of autocorrelated time series. Several state space models for nonseasonal and seasonal time series, which belong to the autoregressive integrated-moving average class, are presented. The Kalman filter is introduced as the recursive data processor for on-line time series forecasting. The estimation problem and initial values determination are discussed, and numerical examples are given. An extension of Brown's adaptive smoothing method for autocorrelated time series through the proposed filtering approach is also presented.

Suggested Citation

  • Tep Sastri, 1985. "A State Space Modeling Approach for Time Series Forecasting," Management Science, INFORMS, vol. 31(11), pages 1451-1470, November.
    • Handle: RePEc:inm:ormnsc:v:31:y:1985:i:11:p:1451-1470
    DOI: 10.1287/mnsc.31.11.1451
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    Cited by:

    1. Xiaodan Zhu & Anh Ninh & Hui Zhao & Zhenming Liu, 2021. "Demand Forecasting with Supply‐Chain Information and Machine Learning: Evidence in the Pharmaceutical Industry," Production and Operations Management, Production and Operations Management Society, vol. 30(9), pages 3231-3252, September.

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    Keywords

    forecasting: time series;

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