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On the transformation of raw time series data: A review

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Abstract

Effective time-series analysis is based on the assumption that the series under investigation is a realisation of a "stationary" stochastic process. In practice, such a stable series can generally only be obtained after some appropriate transformation of the raw data. Two types of non-stationarity can be removed by, respectively, linear and non-linear transformation. These are "homogeneous" non-stationarity and variance instability. The first can be dealt with by backshift operator methods, whilst the second is usually carried out by the approach of Box and Cox, though an easier way is given. The loss of optimal properties, on transforming back to the original situation, can be offset by suitably biasing the results.

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References

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  1. Civil Service Department, Civil Service College, 11 Belgrave Road, SW1V 1RB, London, U.K.

    O. D. Anderson

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  1. O. D. Anderson
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Anderson, O.D. On the transformation of raw time series data: A review. Statistische Hefte 17, 285–289 (1976). https://doi.org/10.1007/BF02923038

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  • DOI: https://doi.org/10.1007/BF02923038

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