Papers › Self-Supervised Contrastive Learning for Long-term Forecasting
Self-Supervised Contrastive Learning for Long-term Forecasting
Junwoo Park, Daehoon Gwak, Jaegul Choo, Edward Choi
Long-term forecasting presents unique challenges due to the time and memory complexity of handling long sequences. Existing methods, which rely on sliding windows to process long sequences, struggle to effectively capture long-term variations that are partially caught within the short window (i.e., outer-window variations). In this paper, we introduce a novel approach that overcomes this limitation by employing contrastive learning and enhanced decomposition architecture, specifically designed to focus on long-term variations. To this end, our contrastive loss incorporates global autocorrelation held in the whole time series, which facilitates the construction of positive and negative pairs in a self-supervised manner. When combined with our decomposition networks, our contrastive learning significantly improves long-term forecasting performance. Extensive experiments demonstrate that our approach outperforms 14 baseline models in multiple experiments over nine long-term benchmarks, especially in challenging scenarios that require a significantly long output for forecasting. Source code is available at https://github.com/junwoopark92/Self-Supervised-Contrastive-Forecsating.
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Tasks
Results from the paper archive 2025-07-28
| Task | Dataset | Model | Metric | Value | Rank at snapshot | Leaderboard | Report |
|---|---|---|---|---|---|---|---|
| Time Series Forecasting | ETTh1 (720) Univariate | AutoCon | MAE | 0.223 | #1 of 12 | Archive leaderboard | report |
| Time Series Forecasting | ETTh1 (720) Univariate | AutoCon | MSE | 0.078 | #1 of 12 | Archive leaderboard | report |
| Time Series Forecasting | ETTh2 (720) Univariate | AutoCon | MAE | 0.344 | #1 of 11 | Archive leaderboard | report |
| Time Series Forecasting | ETTh2 (720) Univariate | AutoCon | MSE | 0.177 | #1 of 11 | Archive leaderboard | report |
Ranks are positions in the archive's leaderboards as they stood at the 2025-07-28 snapshot. Results published since then are not among these rows, so a rank here is not a current standing.
Methods
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