Dense Bag-of-Temporal-SIFT-Words for Time Series Classification

TL;DR

This paper introduces a novel time series classification method based on adapting the SIFT image descriptor to create a dense Bag-of-Words model, improving accuracy over classical techniques.

Contribution

It proposes a new approach that adapts the SIFT framework for time series, incorporating dense feature extraction and normalization for enhanced classification performance.

Findings

Outperforms classical point-to-point distance methods

Normalized Bag-of-Words improves accuracy

Dense feature extraction enhances classification results

Abstract

Time series classification is an application of particular interest with the increase of data to monitor. Classical techniques for time series classification rely on point-to-point distances. Recently, Bag-of-Words approaches have been used in this context. Words are quantized versions of simple features extracted from sliding windows. The SIFT framework has proved efficient for image classification. In this paper, we design a time series classification scheme that builds on the SIFT framework adapted to time series to feed a Bag-of-Words. We then refine our method by studying the impact of normalized Bag-of-Words, as well as densely extract point descriptors. Proposed adjustements achieve better performance. The evaluation shows that our method outperforms classical techniques in terms of classification.