Discovery of Meaningful Rules in Time Series

Abstract

The ability to make predictions about future events is at the heart of much of science; so, it is not surprising that prediction has been a topic of great interest in the data mining community for the last decade. Most of the previous work has attempted to predict the future based on the current <i>value</i> of a stream. However, for many problems the actual values are irrelevant, whereas the <i>shape</i> of the current time series pattern may foretell the future. The handful of research efforts that consider this variant of the problem have met with limited success. In particular, it is now understood that most of these efforts allow the discovery of spurious rules. We believe the reason why rule discovery in real-valued time series has failed thus far is because most efforts have more or less indiscriminately applied the ideas of <i>symbolic</i> stream rule discovery to <i>real-valued</i> rule discovery. In this work, we show why these ideas are not directly suitable for rule discovery in time series. Beyond our novel definitions/representations, which allow for meaningful and extendable specifications of rules, we further show novel algorithms that allow us to quickly discover high quality rules in very large datasets that accurately predict the occurrence of future events.

DOI: 10.1145/2783258.2783306
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@inproceedings{ShokoohiYekta2015DiscoveryOM, title={Discovery of Meaningful Rules in Time Series}, author={Mohammad Shokoohi-Yekta and Yanping Chen and Bilson J. L. Campana and Bing Hu and Jesin Zakaria and Eamonn J. Keogh}, booktitle={KDD}, year={2015} }