AI Insight
This study demonstrates that a standard patch Transformer architecture with a straightforward training protocol can achieve zero-shot forecasting performance comparable to state-of-the-art Time Series Foundation Models. Through comprehensive ablation studies, the researchers identified that pretraining data composition, contiguous patch masking, and context length are critical factors for forecasting accuracy, rather than complex architectural innovations. The findings suggest that proper training methodology and data engineering can be more important than novel architectural designs for time series prediction tasks.
Why it matters
These results provide the research community with a transparent, reproducible baseline for developing time series forecasting models, potentially reducing unnecessary architectural complexity in future models. The work helps clarify which components genuinely contribute to performance improvements in time series foundation models, enabling more efficient development of forecasting systems for applications in finance, weather prediction, and other domains requiring temporal predictions.
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⚠️ Preprint – Noch nicht peer-reviewed
Dieser Artikel wurde noch nicht von unabhängigen Experten begutachtet. Die Ergebnisse sind vorläufig und sollten mit Vorsicht interpretiert werden.
Abstract: The recent surge of interest in Time Series Foundation Models has rapidly advanced the field. Yet, heterogeneous training setups across studies make it difficult to determine whether performance improvements came from architectural innovations or effective data engineering. In this work, we investigate whether a standard patch Transformer architecture, paired with a straightforward training protocol, can match the zero-shot forecasting performance of state-of-the-art TSFMs. We conduct a comprehensive ablation study covering model scaling, data composition, and training techniques to examine the ingredients behind this performance. Our findings show that pretraining data composition, contiguous patch masking, and context length have a substantial effect on forecasting accuracy, and that the generic architecture itself can deliver high performance with good scalability. By strictly controlling these experimental variables, we provide comprehensive empirical results on model scaling across multiple dimensions. Furthermore, we establish that the effectiveness of this training protocol generalizes robustly across benchmarks and alternative architectures. We release our pretrained models and document the detailed training protocol and findings to establish a transparent, reproducible baseline for future research.