The minimal p-values for your baselines suggest that the main difference from the forecast precision of the Decompose & Conquer model and that in the baselines is statistically major. The outcomes highlighted the predominance on the Decompose & Conquer design, particularly when in comparison with the Autoformer and Informer types, where by the real difference in performance was most pronounced. Within this set of checks, the importance stage ( α
A solitary linear layer is adequately robust to model and forecast time collection facts furnished it has been appropriately decomposed. Thus, we allotted only one more info linear layer for every element On this research.
?�乎，�?每�?次点?�都?�满?�义 ?��?�?��?�到?�乎，发?�问题背?�的世界??Even so, these studies typically overlook straightforward, but really successful approaches, like decomposing a time series into its constituents being a preprocessing move, as their concentrate is principally to the forecasting model.
今般??��定取得に?�り住宅?�能表示?�準?�従?�た?�能表示?�可?�な?�料?�な?�ま?�た??While the aforementioned conventional strategies are well-known in many realistic scenarios because of their trustworthiness and performance, they tend to be only suited to time sequence having a singular seasonal sample.