Beyond Holistic Models: Systematic Component-level Benchmarking of Deep Multivariate Time-Series Forecasting
arXiv:2605.26562v1 Announce Type: new Abstract: While previous research in multivariate time series forecasting has focused on developing complex holistic models, this work advocates for a shift toward a granular, component-level understanding of their impacts. We propose TSCOMP, the first large-scale benchmark that systematically deconstructs deep forecasting methods into their core, fine-grained components--spanning series preprocessing, encoding strategies, network architectures including specific and large time-series models, and optimization methods. Using constrained orthogonal experimen
The proliferation of complex deep learning models in time-series forecasting necessitates more granular, interpretable performance analysis to drive further innovation.
This new benchmarking approach offers a systematic way to understand the impact of individual model components, allowing researchers and practitioners to optimize specific elements rather than relying on holistic, 'black box' improvements.
The focus shifts from developing entirely new holistic models to methodically improving and combining specific components, potentially accelerating progress in time-series forecasting across various applications.
- · AI/ML researchers
- · Data scientists
- · Companies relying on forecasting (e.g., finance, logistics, energy)
- · Cloud AI platform providers
- · Researchers focused solely on holistic model development without component analy
- · Organizations using suboptimal forecasting models due to lack of granular insigh
Improved accuracy and efficiency in multivariate time-series forecasting tasks across industries.
Increased adoption of specialized forecasting components, leading to new software libraries and frameworks.
More robust and explainable AI systems for critical predictive analytics, reducing operational risks.
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