A Generative Model For Time Series Discretization Based On Multiple Normal Distributions

Sunil Gandhi, Tim Oates, Arnold P. Boedihardjo, Crystal Chen, Jessica Lin, Pavel Senin, Susan Frankenstein, Xing Wang

Abstract: Discretization is a crucial first step in several time series mining applications. Our research proposes a novel method to discretize time series data and develops a similarity score based on the discretized representation. The similarity score allows us to compare two time series sequences and enables us to perform pattern learning tasks such as clustering, classification, and anomaly detection. We propose a generative model for discretization based on multiple normal distributions and create an optimization technique to learn parameters of these normal distributions. To show the effectiveness of our approach, we perform comprehensive experiments in classifying datasets from the UCR time series repository.