Does Normalization Choice Matter for Causal Large Time-Series Models?

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

Source: arXiv cs.LG — read the full report at the original publisher.

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