# Capturing Evolution Genes for Time Series Data

## TL;DR

This paper introduces a novel framework for modeling time series data by capturing latent user behavior patterns, called evolution genes, which improve prediction accuracy and provide interpretability.

## Contribution

It proposes a unified approach to identify evolution genes in time series using classification and adversarial generation, enhancing both prediction and explanation capabilities.

## Key findings

- Achieved an average +10.56% F1 score improvement
- Effectively captures latent user behaviors
- Provides interpretable insights into time series evolution

## Abstract

The modeling of time series is becoming increasingly critical in a wide variety of applications. Overall, data evolves by following different patterns, which are generally caused by different user behaviors. Given a time series, we define the evolution gene to capture the latent user behaviors and to describe how the behaviors lead to the generation of time series. In particular, we propose a uniform framework that recognizes different evolution genes of segments by learning a classifier, and adopt an adversarial generator to implement the evolution gene by estimating the segments' distribution. Experimental results based on a synthetic dataset and five real-world datasets show that our approach can not only achieve a good prediction results (e.g., averagely +10.56% in terms of F1), but is also able to provide explanations of the results.