Trend-Aligned Feature Correlation: A Holistic Feature Relevance Metric on Time Series Data

Existing time series correlation research methods tend to over-rely on global statistical features while neglecting the dynamic variation of data along the temporal dimension, particularly falling short in trend inflection point identification and complex correlation discrimination. This study proposes an innovative Trend-Aligned Feature Correlation (TAFC) matching method that overcomes the limitations of traditional correlation analysis. TAFC employs a refined data preprocessing pipeline and introduces two unique metrics: the Trend Overlap Index (Q) and the Numerical Correlation Metric (P). The P metric effectively captures numerical correlation between data by combining a Normalized Difference Metric (NDM) with the Pearson correlation coefficient. Furthermore, TAFC introduces a customized granularity smoothing mechanism to balance trend feature preservation with noise suppression, aiming to comprehensively assess the multidimensional, multi-granularity correlation of time series data. This study constructs simulated scenario data based on chi-square and multimodal distributions for experimental validation, covering 36 sets of simulated data. Results demonstrate that the method can significantly differentiate datasets with distinct trend characteristics; the introduction of granularity has been validated to help make trend features more prominent; simultaneously, under the introduction of 5% random noise, the algorithmic error is controlled within the range of 5.60%–12.24%. This work provides a novel and precise approach for computer-aided time series data analysis with significant application value.