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How to handle time features shall be the core question of any time seriesforecasting model. Ironically, it is often ignored or misunderstood bydeep-learning based models, even those baselines which are state-of-the-art.This behavior makes their inefficient, untenable and unstable. In this paper,we rigorously analyze three prevalent but deficient/unfounded deep time seriesforecasting mechanisms or methods from the view of time series properties,including normalization methods, multivariate forecasting and input sequencelength. Corresponding corollaries and solutions are given on both empirical andtheoretical basis. We thereby propose a novel time series forecasting network,i.e. RTNet, on the basis of aforementioned analysis. It is general enough to becombined with both supervised and self-supervised forecasting format. Thanks tothe core idea of respecting time series properties, no matter in whichforecasting format, RTNet shows obviously superior forecasting performancescompared with dozens of other SOTA time series forecasting baselines in threereal-world benchmark datasets. By and large, it even occupies less timecomplexity and memory usage while acquiring better forecasting accuracy. Thesource code is available at https://github.com/OrigamiSL/RTNet.

Respecting Time Series Properties Makes Deep Time Series Forecasting Perfect