  Time series underwent the transition from statistics to deep learning, as did
many other machine learning fields. Although it appears that the accuracy has
been increasing as the model is updated in a number of publicly available
datasets, it typically only increases the scale by several times in exchange
for a slight difference in accuracy. Through this experiment, we point out a
different line of thinking, time series, especially long-term forecasting, may
differ from other fields. It is not necessary to use extensive and complex
models to grasp all aspects of time series, but to use pure models to grasp the
core rules of time series changes. With this simple but effective idea, we
created PureTS, a network with three pure linear layers that achieved
state-of-the-art in 80% of the long sequence prediction tasks while being
nearly the lightest model and having the fastest running speed. On this basis,
we discuss the potential of pure linear layers in both phenomena and essence.
The ability to understand the core law contributes to the high precision of
long-distance prediction, and reasonable fluctuation prevents it from
distorting the curve in multi-step prediction like mainstream deep learning
models, which is summarized as a pure linear neural network that avoids
over-fluctuating. Finally, we suggest the fundamental design standards for
lightweight long-step time series tasks: input and output should try to have
the same dimension, and the structure avoids fragmentation and complex
operations.
