COCALITE: A Hybrid Model COmbining CAtch22 and LITE for Time Series Classification

Time series classification has achieved significant advancements through deep learning models; however, these models often suffer from high complexity and computational costs. To address these challenges while maintaining effectiveness, we introduce COCALITE, an innovative hybrid model that combines the efficient LITE model with an augmented version incorporating Catch22 features during training. COCALITE operates with only 4.7% of the parameters of the state-of-the-art Inception model, significantly reducing computational overhead. By integrating these complementary approaches, COCALITE leverages both effective feature engineering and deep learning techniques to enhance classification accuracy. Our extensive evaluation across 128 datasets from the UCR archive demonstrates that COCALITE achieves competitive performance, offering a compelling solution for resource-constrained environments.