The reduced p-values for your baselines suggest that the main difference from the forecast precision of your Decompose & Conquer model and that from the baselines is statistically considerable. The final results highlighted the predominance of your Decompose & Conquer model, specially when in comparison with the Autoformer and Informer styles, exactly where the real difference in performance was most pronounced. Within this set of tests, the importance stage ( α
If the scale of seasonal changes or deviations across the craze?�cycle continue being consistent regardless of the time series level, then the additive decomposition is appropriate.
?�乎，�?每�?次点?�都?�满?�义 ?��?�?��?�到?�乎，发?�问题背?�的世界??Having said that, these reports often forget about simple, but very helpful strategies, for instance decomposing a time collection into its constituents as being a preprocessing action, as their aim is mainly within the forecasting design.
Home windows - The lengths of each seasonal smoother with regard to every time period. If these are significant then the seasonal element will clearly show considerably less variability over time. Have to be here odd. If None a list of default values based on experiments in the original paper [1] are utilised.