Lossless Compression: A New Benchmark for Time Series Model Evaluation

The evaluation of time series models has traditionally focused on four canonical tasks: forecasting, imputation, anomaly detection, and classification. Although these tasks have made significant progress, they primarily assess task-specific performance and do not rigorously measure whether a model captures the full generative distribution of the data. We introduce lossless compression as a new paradigm for evaluating time series models, grounded in Shannon's source coding theorem. This perspective establishes a direct equivalence between optimal compression length and the negative log-likelihood, providing a strict and unified information-theoretic criterion for modeling capacity. Then we define a standardized evaluation protocol and metrics. We further propose and open-source a comprehensive evaluation framework TSCom-Bench, which enables the rapid adaptation of time series models as backbones for lossless compression. Experiments across diverse datasets on state-of-the-art models, including TimeXer, iTransformer, and PatchTST, demonstrate that compression reveals distributional weaknesses overlooked by classic benchmarks. These findings position lossless compression as a principled task that complements and extends existing evaluations for time series modeling. Time series modeling is a fundamental branch of machine learning with critical applications in finance, healthcare, climate science, and industrial operations Sakib et al. (2025). Recent advances in deep learning have pushed the field from early recurrent and convolutional networks to models utilizing self-attention and hybrid architectures, which demonstrate remarkable performance across a variety of settings Kim et al. (2025); Mahmoud & Mohammed (2024). However, a central challenge remains unresolved: how to systematically and rigorously evaluate their modeling capacity. Currently, the time series research widely relies on four canonical benchmark tasks: forecasting, anomaly detection, imputation, and classification Jin et al. (2024). While these tasks have undeniably advanced the field, they exhibit an inherent limitation: their optimization objectives do not directly correspond to a model's ability to capture the global statistical structure of a sequence. In other words, they primarily validate task-specific functionality but fail to provide a comprehensive assessment of distributional modeling capacity.