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An Automated Robust Method for Estimating Trend and Detecting Changes in Trend for Short Time Series

In: Proceedings of the First US/Japan Conference on the Frontiers of Statistical Modeling: An Informational Approach

Author

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  • T. Atilgan

    (AT&T Bell Laboratories, Customer Analysis Systems Group)

Abstract

Summary In analyzing very large numbers of short time series (30 to 60 measurements per time series) we are often faced with outliers missing observations structural changes, seasonality, requiring us to develop a time series analysis approach which should be, robust to outliers have an automatic decision making mechanism on how much smoothing needs to be done in the trend and seasonal component of time series, handle missing observations, handle missing observations, be flexible enough, when needed, to handle structural changes.

Suggested Citation

  • T. Atilgan, 1994. "An Automated Robust Method for Estimating Trend and Detecting Changes in Trend for Short Time Series," Springer Books, in: H. Bozdogan & S. L. Sclove & A. K. Gupta & D. Haughton & G. Kitagawa & T. Ozaki & K. Tanabe (ed.), Proceedings of the First US/Japan Conference on the Frontiers of Statistical Modeling: An Informational Approach, chapter 10, pages 169-186, Springer.
    • Handle: RePEc:spr:sprchp:978-94-011-0866-9_11
    DOI: 10.1007/978-94-011-0866-9_11
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