A Global Post hoc XAI Method for Interpreting LSTM Using Deterministic Finite State Automata

We propose a global post-hoc XAI method to interpret Long Short-Term Memory (LSTM) models for univariate time series classification. Our approach integrates Symbolic Aggregate approXimation (SAX) to convert continuous time series into symbolic representations during preprocessing. We then apply k-means clustering to the activated hidden states of the LSTM, from which we extract Deterministic Finite Automata (DFA), which provides a transparent and interpretable explanation of the model’s decision-making process. Experiments on synthetic and real-world datasets demonstrate high fidelity between DFA and LSTM, with enhanced interpretability for high-stakes domains like healthcare and power demand forecasting.