TS3IM: Unveiling Structural Similarity in Time Series through Image Similarity Assessment Insights

TL;DR

TS3IM is a new similarity measure for time series data inspired by image analysis techniques, capturing structural nuances to improve accuracy in applications like anomaly detection and forecasting.

Contribution

The paper introduces TS3IM, a novel structural similarity index for time series, enhancing analysis by evaluating multiple dimensions of similarity beyond traditional metrics.

Findings

TS3IM is 1.87 times more similar to DTW in evaluation.

TS3IM improves adversarial recognition accuracy by over 50%.

Provides a more comprehensive and nuanced similarity assessment.

Abstract

In the realm of time series analysis, accurately measuring similarity is crucial for applications such as forecasting, anomaly detection, and clustering. However, existing metrics often fail to capture the complex, multidimensional nature of time series data, limiting their effectiveness and application. This paper introduces the Structured Similarity Index Measure for Time Series (TS3IM), a novel approach inspired by the success of the Structural Similarity Index Measure (SSIM) in image analysis, tailored to address these limitations by assessing structural similarity in time series. TS3IM evaluates multiple dimensions of similarity-trend, variability, and structural integrity-offering a more nuanced and comprehensive measure. This metric represents a significant leap forward, providing a robust tool for analyzing temporal data and offering more accurate and comprehensive sequence analysis and decision support in fields such as monitoring power consumption, analyzing traffic flow, and adversarial recognition. Our extensive experimental results also show that compared with traditional methods that rely heavily on computational correlation, TS3IM is 1.87 times more similar to Dynamic Time Warping (DTW) in evaluation results and improves by more than 50% in adversarial recognition.