  Short fixed-length inputs are the main bottleneck of deep learning methods in
long time-series forecasting tasks. Prolonging input length causes overfitting,
rapidly deteriorating accuracy. Our research indicates that the overfitting is
a combination reaction of the multi-scale pattern coupling in time series and
the fixed focusing scale of current models. First, we find that the patterns
exhibited by a time series across various scales are reflective of its
multi-periodic nature, where each scale corresponds to specific period length.
Second, We find that the token size predominantly dictates model behavior, as
it determines the scale at which the model focuses and the context size it can
accommodate. Our idea is to decouple the multi-scale temporal patterns of time
series and to model each pattern with its corresponding period length as token
size. We introduced a novel series-decomposition module(MPSD), and a
Multi-Token Pattern Recognition neural network(MTPR), enabling the model to
handle \textit{inputs up to $10\times$ longer}. Sufficient context enhances
performance(\textit{38% maximum precision improvement}), and the decoupling
approach offers \textit{Low complexity($0.22\times$ cost)} and \textit{high
interpretability}.
