Unsupervised Time‐Event Probabilistic Classification Using Large Panels of Time Series

ABSTRACT This study presents a framework to perform unsupervised time‐event probabilistic classification using time series data of large cross‐sectional dimension. These datasets often exhibit complexities such as non‐linearities, structural breaks, asynchronicity, missing data, and outliers; which hampers their analysis and modeling. To address these challenges, the proposed approach integrates symbolic analysis, compositional data analysis, and Markov‐switching time series modeling into a unified methodology. A Monte Carlo simulation study demonstrates the robustness of the method in various challenging scenarios. The practical applicability of the framework is illustrated through two economic case studies: (i) identifying recurrent recession and expansion regimes in the US economy using state‐level data, and (ii) detecting breakpoints in high‐volatility episodes in the US stock market using data from all assets in the S&P 500 index.