🤖 AI Summary
This work addresses the limitations of univariate time series classification stemming from singular input representations and insufficient model scalability by proposing LiteMV, the first scalable framework that jointly leverages multi-representation learning and multi-scale modeling. The framework comprises three core components: MSNet, which emphasizes robustness and probabilistic calibration; LS-Net, an efficient and lightweight architecture; and a LiteMV cross-representation interaction mechanism tailored for univariate signals. Through systematic integration of multi-scale convolutions, multi-representation fusion, and calibration-aware optimization, extensive evaluation across 142 benchmark datasets demonstrates that LiteMV achieves the highest average accuracy, MSNet attains the best calibration performance (lowest negative log-likelihood), and LS-Net offers an optimal trade-off between accuracy and efficiency. Pareto analysis further confirms the framework’s ability to flexibly balance accuracy, calibration quality, and computational constraints.
📝 Abstract
Time series classification (TSC) performance depends not only on architectural design but also on the diversity of input representations. In this work, we propose a scalable multi-scale convolutional framework that systematically integrates structured multi-representation inputs for univariate time series.
We introduce two architectures: MSNet, a hierarchical multi-scale convolutional network optimized for robustness and calibration, and LS-Net, a lightweight variant designed for efficiency-aware deployment. In addition, we adapt LiteMV -- originally developed for multivariate inputs -- to operate on multi-representation univariate signals, enabling cross-representation interaction.
We evaluate all models across 142 benchmark datasets under a unified experimental protocol. Critical Difference analysis confirms statistically significant performance differences among the top models. Results show that LiteMV achieves the highest mean accuracy, MSNet provides superior probabilistic calibration (lowest NLL), and LS-Net offers the best efficiency-accuracy tradeoff. Pareto analysis further demonstrates that multi-representation multi-scale modeling yields a flexible design space that can be tuned for accuracy-oriented, calibration-oriented, or resource-constrained settings.
These findings establish scalable multi-representation multi-scale learning as a principled and practical direction for modern TSC. Reference implementation of MSNet and LS-Net is available at: https://github.com/alagoz/msnet-lsnet-tsc