A prediction scheme using perceptually important points and dynamic time warping

Journal article


Tsinaslanidis, P. and Kugiumtzis, D. 2014. A prediction scheme using perceptually important points and dynamic time warping. Expert Systems with Applications. 41 (15), pp. 6848-6860. https://doi.org/10.1016/j.eswa.2014.04.028
AuthorsTsinaslanidis, P. and Kugiumtzis, D.
Abstract

An algorithmic method for assessing statistically the efficient market hypothesis (EMH) is developed based on two data mining tools, perceptually important points (PIPs) used to dynamically segment price series into subsequences, and dynamic time warping (DTW) used to find similar historical subsequences. Then predictions are made from the mappings of the most similar subsequences, and the prediction error statistic is used for the EMH assessment. The predictions are assessed on simulated price paths composed of stochastic trend and chaotic deterministic time series, and real financial data of 18 world equity markets and the GBP/USD exchange rate. The main results establish that the proposed algorithm can capture the deterministic structure in simulated series, confirm the validity of EMH on the examined equity indices, and indicate that prediction of the exchange rates using PIPs and DTW could beat at cases the prediction of last available price.

Year2014
JournalExpert Systems with Applications
Journal citation41 (15), pp. 6848-6860
PublisherElsevier
ISSN0957-4174
Digital Object Identifier (DOI)https://doi.org/10.1016/j.eswa.2014.04.028
Publication dates
Print01 Nov 2014
Publication process dates
Deposited10 Sep 2014
Accepted author manuscript
Output statusPublished
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https://repository.canterbury.ac.uk/item/871vz/a-prediction-scheme-using-perceptually-important-points-and-dynamic-time-warping

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