GeoStat Representations of Time Series for Fast Classification

TL;DR

This paper introduces GeoStat, a computationally efficient time series representation method based on differential geometric statistics, enabling fast classification with competitive accuracy using simple classifiers like KNN and SVM.

Contribution

GeoStat provides a novel, parameter-light representation of time series that achieves state-of-the-art classification performance with minimal computational resources.

Findings

GeoStat achieves competitive results on various datasets.

Simple classifiers on GeoStat features outperform complex models in many cases.

Effective on a challenging vessel classification dataset with limited training data.

Abstract

Recent advances in time series classification have largely focused on methods that either employ deep learning or utilize other machine learning models for feature extraction. Though successful, their power often comes at the requirement of computational complexity. In this paper, we introduce GeoStat representations for time series. GeoStat representations are based off of a generalization of recent methods for trajectory classification, and summarize the information of a time series in terms of comprehensive statistics of (possibly windowed) distributions of easy to compute differential geometric quantities, requiring no dynamic time warping. The features used are intuitive and require minimal parameter tuning. We perform an exhaustive evaluation of GeoStat on a number of real datasets, showing that simple KNN and SVM classifiers trained on these representations exhibit surprising performance relative to modern single model methods requiring significant computational power, achieving state of the art results in many cases. In particular, we show that this methodology achieves good performance on a challenging dataset involving the classification of fishing vessels, where our methods achieve good performance relative to the state of the art despite only having access to approximately two percent of the dataset used in training and evaluating this state of the art.