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.

时间序列分析中，精确测量相似性对于预测、异常检测和聚类等应用至关重要。本文引入了一种基于结构相似性指数度量的时间序列结构相似性指标测量（TS3IM），通过评估趋势、变异性和结构完整性等多个维度的相似性，提供了更细致和全面的测量方法。这一新指标在分析时间数据、提供更准确和全面的序列分析和决策支持方面有着重要的作用，在监测能耗、分析交通流量和对抗性识别等领域取得了实验结果方面显著的进展。