Abstract
This chapter proposes and analyzes a new method for predicting the direction of a timeseries, that is, the relative position of future observations with respect to past coordinates, a problem of obvious interest to financial forecasters. The method involves two steps: an embedding step from real-valued observations to discrete values and a prediction step based on statistical inference. Both of these are explained in detail and rigorously justified. Finally, the method is applied to two illustrative time series: the daily closing prices of the S&P500 market index (for the period 1995–2001) and the quarterly growth rates for the US gross domestic product from 1959 till 2000. The results obtained for these two cases are extremely encouraging.
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© 2004 Springer-Verlag Berlin Heidelberg
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Thomakos, D.D. (2004). Predicting the Direction of a Time Series. In: Wille, L.T. (eds) New Directions in Statistical Physics. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-662-08968-2_1
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DOI: https://doi.org/10.1007/978-3-662-08968-2_1
Publisher Name: Springer, Berlin, Heidelberg
Print ISBN: 978-3-642-07739-5
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