Name
Title | ||
|---|---|---|
CoST: Contrastive Learning of Disentangled Seasonal-Trend Representations for Time Series Forecasting |
Abstract | ||
|---|---|---|
Deep learning has been actively studied for time series forecasting, and the mainstream paradigm is based on the end-to-end training of neural network architectures, ranging from classical LSTM/RNNs to more recent TCNs and Transformers. Motivated by the recent success of representation learning in computer vision and natural language processing, we argue that a more promising paradigm for time series forecasting, is to first learn disentangled feature representations, followed by a simple regression fine-tuning step -- we justify such a paradigm from a causal perspective. Following this principle, we propose a new time series representation learning framework for time series forecasting named CoST, which applies contrastive learning methods to learn disentangled seasonal-trend representations. CoST comprises both time domain and frequency domain contrastive losses to learn discriminative trend and seasonal representations, respectively. Extensive experiments on real-world datasets show that CoST consistently outperforms the state-of-the-art methods by a considerable margin, achieving a 21.3\% improvement in MSE on multivariate benchmarks. It is also robust to various choices of backbone encoders, as well as downstream regressors. |
Year | Venue | Keywords |
|---|---|---|
2022 | International Conference on Learning Representations (ICLR) | Time Series,Representation Learning,Forecasting,Self-Supervised Learning |
DocType | Citations | PageRank |
Conference | 0 | 0.34 |
References | Authors | |
0 | 5 | |
Name | Order | Citations | PageRank |
|---|---|---|---|
| Gerald Woo | 1 | 0 | 0.34 |
| Chenghao Liu | 2 | 334 | 32.66 |
| Doyen Sahoo | 3 | 83 | 9.94 |
| Akshat Kumar | 4 | 0 | 0.34 |
| Steven C. H. Hoi | 5 | 3830 | 174.61 |