Discovery of Meaningful Rules in Time Series

TLDR

Prediction is central to science, yet most time‑series research predicts future values from the current value, ignoring that the shape of the current pattern may better forecast the future, a variant that has seen limited success. The authors argue that rule discovery in real‑valued time series has failed because previous work indiscriminately applied symbolic stream rule discovery concepts to real‑valued data. They introduce novel definitions and representations for rules and present efficient algorithms that enable rapid discovery of high‑quality rules in large datasets. Their experiments show that earlier methods generate spurious rules, whereas the new approach yields meaningful, extendable rules that accurately predict future events.

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 value of a stream. However, for many problems the actual values are irrelevant, whereas the shape 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 symbolic stream rule discovery to real-valued 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.