arXiv:2606.09954v1 Announce Type: new Abstract: Large models for time-series forecasting have been emerged as a promising paradigm for training models on heterogeneous collections of signals. These models typically rely on causal autoregressive architectures, where each observation is sequentially predicted from past. In practice, real-world time-series exhibit non-stationarities, which significantly influence predictive performance. To mitigate this, normalization is commonly employed. However, in efficient causal settings it might induce information leakage from future observations during tr
This paper addresses a fundamental challenge for large time-series models, which are gaining prominence in various fields, highlighting an active area of research to improve their practical application.
Improving the accuracy and reliability of large time-series models is crucial for forecasting in finance, logistics, and climate, directly impacting strategic decision-making across sectors.
Understanding the effect of normalization choices can lead to more robust and accurate causal time-series models, mitigating an important source of error and information leakage.
- · AI researchers
- · Time-series forecasting platforms
- · Industries relying on predictive analytics
- · Developers using suboptimal normalization in causal models
Refined guidelines for normalization in large causal time-series models will emerge, improving model performance.
More reliable forecasting will lead to better operational efficiencies and risk management in various applications.
Increased adoption of large time-series models as their practical limitations are better understood and addressed.
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