ECOST: Enhanced CoST Framework for Fast and Accurate Time Series Forecasting

We introduce an enhanced forecasting framework for time series using contrastive learning. This method builds on the existing CoST (Contrastive Learning of Disentangled Seasonal-Trend Representations) framework, which, despite its promising performance, is still encumbered by the challenges posed by the intricate nature of time series data. In our proposed framework, we bypass the need for a backbone encoder and directly perform time series decomposition to extract the trend and detrended subseries. These components subsequently undergo independent trend and seasonal feature extraction. This approach ensures a more robust, efficient, and direct representation of inherent time series characteristics. We incorporate Reversible Instance Normalization (RevIN) to improve forecasting accuracy and account for potential distribution bias. Additionally, a new concept, the ‘trend queue’, is proposed for storing past trend features, improving the learning of trend nuances. Our ECoST model has shown significant improvements, with an increase in prediction accuracy by 8.5% and a 74% enhancement in training time efficiency compared to the CoST model. These results were validated through experiments conducted on several real-world time series datasets. This underscores the effectiveness of our approach in providing a more robust and efficient time series forecasting methodology, thereby setting a new benchmark in the field of contrastive learning for time series data.