Improved dynamic time warping for fire safety emergency response: A robust and interpretable extension

Multivariate time series classification (MTSC) has gained considerable attention in the fire safety industry, enabling the development of specialized algorithms based on the unique characteristics of fire-related time series. Traditional feature representation techniques (e.g., SAX , DTW) often incur high computational costs, offer limited interpretability , and focus primarily on shape information. Moreover, many TSC algorithms struggle with robustness under distributional shifts across diverse fire environments. We propose a new algorithm that strengthens robust feature representations and modeling, enabling groupwise feature importance analysis for fire classification. Our approach captures both shape and amplitude information, along with first-order differences, by integrating SAX and DTW with random SAX baselines and masks to remove potential noise. We then apply a sequence of groupwise logistic classifiers, using penalties on dimensionally grouped features to capture group effects. Numerical experiments on specific fire safety scenarios and the UEA MTS Archive confirm the model’s robustness and interpretability. Furthermore, we have developed AI-driven software that helps first responders detect burning materials from gas emissions, guided by the Emergency Response Guidebook (ERG). This integrated solution equips emergency teams with essential tools for timely and informed fire response. • A distance-based and dictionary-based machine learning model for fire safety classification. • Achieved high accuracy and robustness under distributional perturbations. • Enabled feature selections and high interpretability.