# Information Theory Inspired Pattern Analysis for Time-series Data

## TL;DR

This paper introduces a novel, information theory-based approach for pattern analysis in multivariate time-series data, demonstrating improved accuracy and efficiency over traditional statistical and probabilistic methods.

## Contribution

The paper presents a highly generalizable method using entropy-based features for pattern detection in complex time-series data, applicable to various scenarios including stochastic state transitions.

## Key findings

- Improved recall rate, F1 score, and accuracy by up to 23.01%.
- Achieved an average reduction of 18.75 times in model parameters.
- Validated approach on human activity data with significant performance gains.

## Abstract

Current methods for pattern analysis in time series mainly rely on statistical features or probabilistic learning and inference methods to identify patterns and trends in the data. Such methods do not generalize well when applied to multivariate, multi-source, state-varying, and noisy time-series data. To address these issues, we propose a highly generalizable method that uses information theory-based features to identify and learn from patterns in multivariate time-series data. To demonstrate the proposed approach, we analyze pattern changes in human activity data. For applications with stochastic state transitions, features are developed based on Shannon's entropy of Markov chains, entropy rates of Markov chains, entropy production of Markov chains, and von Neumann entropy of Markov chains. For applications where state modeling is not applicable, we utilize five entropy variants, including approximate entropy, increment entropy, dispersion entropy, phase entropy, and slope entropy. The results show the proposed information theory-based features improve the recall rate, F1 score, and accuracy on average by up to 23.01% compared with the baseline models and a simpler model structure, with an average reduction of 18.75 times in the number of model parameters.