Efficient Temporal Pattern Mining in Big Time Series Using Mutual Information -- Full Version

TL;DR

This paper introduces an efficient method for mining frequent temporal patterns from large time series datasets, leveraging mutual information for pruning, resulting in faster and more scalable analysis.

Contribution

It presents the FTPMf TS framework and the HTPGM algorithm, including an approximate version using mutual information for improved scalability and efficiency.

Findings

HTPGM outperforms baselines in runtime and memory usage

Approximate HTPGM is up to 100 times faster with high accuracy

Method scales effectively to large time series datasets

Abstract

Very large time series are increasingly available from an ever wider range of IoT-enabled sensors deployed in different environments. Significant insights can be gained by mining temporal patterns from these time series. Unlike traditional pattern mining, temporal pattern mining (TPM) adds event time intervals into extracted patterns, making them more expressive at the expense of increased mining time complexity. Existing TPM methods either cannot scale to large datasets, or work only on pre-processed temporal events rather than on time series. This paper presents our Frequent Temporal Pattern Mining from Time Series (FTPMf TS) approach which provides: (1) The end-to-end FTPMf TS process taking time series as input and producing frequent temporal patterns as output. (2) The efficient Hierarchical Temporal Pattern Graph Mining (HTPGM) algorithm that uses efficient data structures for fast support and confidence computation, and employs effective pruning techniques for significantly faster mining. (3) An approximate version of HTPGM that uses mutual information, a measure of data correlation known from information theory, to prune unpromising time series from the search space. (4) An extensive experimental evaluation showing that HTPGM outperforms the baselines in runtime and memory consumption, and can scale to big datasets. The approximate HTPGM is up to two orders of magnitude faster and less memory consuming than the baselines, while retaining high accuracy.