TSGym: Design Choices for Deep Multivariate Time-Series Forecasting
This work addresses the issue of obscured individual contributions in MTSF research for practitioners and researchers, offering a more effective and transferable approach, though it is incremental in improving model design and evaluation.
The paper tackles the problem of evaluating deep multivariate time-series forecasting (MTSF) methods by systematically decomposing them into fine-grained components and proposing TSGym, an automated solution for component selection and model construction, which significantly outperforms existing state-of-the-art MTSF and AutoML methods.
Recently, deep learning has driven significant advancements in multivariate time series forecasting (MTSF) tasks. However, much of the current research in MTSF tends to evaluate models from a holistic perspective, which obscures the individual contributions and leaves critical issues unaddressed. Adhering to the current modeling paradigms, this work bridges these gaps by systematically decomposing deep MTSF methods into their core, fine-grained components like series-patching tokenization, channel-independent strategy, attention modules, or even Large Language Models and Time-series Foundation Models. Through extensive experiments and component-level analysis, our work offers more profound insights than previous benchmarks that typically discuss models as a whole. Furthermore, we propose a novel automated solution called TSGym for MTSF tasks. Unlike traditional hyperparameter tuning, neural architecture searching or fixed model selection, TSGym performs fine-grained component selection and automated model construction, which enables the creation of more effective solutions tailored to diverse time series data, therefore enhancing model transferability across different data sources and robustness against distribution shifts. Extensive experiments indicate that TSGym significantly outperforms existing state-of-the-art MTSF and AutoML methods. All code is publicly available on https://github.com/SUFE-AILAB/TSGym.