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Pytorch implementation of NIPS'23 paper: Adaptive Normalization for Non-stationary Time Series Forecasting: A Temporal Slice Perspective

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Is there any basis for Slicing Length Selection of different data sets? Is the cycle and other information referenced?

Thank you for your great work! I'm wondering, why are there only self.model.load_state_dict() and no self.statistics_pred.load_state_dict() in def test()? Isn't it just using some randomly initialized weights?