Interpretable Structural Stability Analysis for Long-Term Cognitive IoT Time-Series Data

Long-term heterogeneous time-series data generated by large-scale sensing and environmental monitoring systems exhibit complex temporal behavior that is not fully captured by prediction-driven learning models. While most existing approaches emphasize short-term forecasting accuracy, comparatively little attention has been given to the analysis of long-term structural stability inherent in such data. In this work, we propose a lightweight, training-free analytical framework for quantifying structural stability in long-duration time-series using stability-preserving preprocessing and interpretable temporal statistics. The proposed method combines total variation regularization with rolling statistical analysis to assess the consistency of local temporal behavior relative to global characteristics over extended time horizons. Structural stability is quantified using a simple yet effective stability index that captures deviations between local and global temporal trends. The framework is evaluated using more than two decades of daily environmental observations, including temperature, relative humidity, and precipitation, obtained from the NASA POWER repository for a representative location in Assam, India. Experimental results demonstrate consistent and systematic reductions in the stability index following preprocessing across all variables, indicating improved temporal consistency without structural distortion. Additional robustness analysis across multiple temporal scales confirms that the proposed framework is insensitive to window size selection and preserves long-term structural behavior. These findings suggest that meaningful insights into temporal stability can be obtained without reliance on model training or predictive learning, making the proposed approach suitable for interpretable, resource-efficient analysis of long-term heterogeneous time-series data. Unlike conventional stability descriptors such as variance-based measures or correlation-based consistency metrics, the proposed stability index directly quantifies local-to-global deviation of temporal descriptors across multiple window scales, enabling interpretable and comparable stability assessment without requiring model training or forecasting error baselines.