  Contrastive learning methods have shown an impressive ability to learn
meaningful representations for image or time series classification. However,
these methods are less effective for time series forecasting, as optimization
of instance discrimination is not directly applicable to predicting the future
state from the history context. Moreover, the construction of positive and
negative pairs in current technologies strongly relies on specific time series
characteristics, restricting their generalization across diverse types of time
series data. To address these limitations, we propose SimTS, a simple
representation learning approach for improving time series forecasting by
learning to predict the future from the past in the latent space. SimTS does
not rely on negative pairs or specific assumptions about the characteristics of
the particular time series. Our extensive experiments on several benchmark time
series forecasting datasets show that SimTS achieves competitive performance
compared to existing contrastive learning methods. Furthermore, we show the
shortcomings of the current contrastive learning framework used for time series
forecasting through a detailed ablation study. Overall, our work suggests that
SimTS is a promising alternative to other contrastive learning approaches for
time series forecasting.
