Papers › Are Self-Attentions Effective for Time Series Forecasting?

Are Self-Attentions Effective for Time Series Forecasting?

27 May 2024arXiv:2405.16877archive 2025-07-28

Dongbin Kim, Jinseong Park, Jaewook Lee, Hoki Kim

Time series forecasting is crucial for applications across multiple domains and various scenarios. Although Transformer models have dramatically advanced the landscape of forecasting, their effectiveness remains debated. Recent findings have indicated that simpler linear models might outperform complex Transformer-based approaches, highlighting the potential for more streamlined architectures. In this paper, we shift the focus from evaluating the overall Transformer architecture to specifically examining the effectiveness of self-attention for time series forecasting. To this end, we introduce a new architecture, Cross-Attention-only Time Series transformer (CATS), that rethinks the traditional Transformer framework by eliminating self-attention and leveraging cross-attention mechanisms instead. By establishing future horizon-dependent parameters as queries and enhanced parameter sharing, our model not only improves long-term forecasting accuracy but also reduces the number of parameters and memory usage. Extensive experiment across various datasets demonstrates that our model achieves superior performance with the lowest mean squared error and uses fewer parameters compared to existing models. The implementation of our model is available at: https://github.com/dongbeank/CATS.

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DecoderLayer dongbeank/cats/models/CATS.py official repository ran MIT (permissive) · 5b900d79dd00feb4 · report
Projection dongbeank/cats/models/CATS.py official repository ran · metamorphic tier: invariant MIT (permissive) · c9e915f73a989d19 · report
QueryAdaptiveMasking dongbeank/cats/models/CATS.py official repository ran · metamorphic tier: invariant fingerprinted MIT (permissive) · 51cadb0ea6fdf45c · report
_MultiheadAttention dongbeank/cats/models/CATS.py official repository ran · metamorphic tier: deterministic fingerprinted MIT (permissive) · 928f4380b09f0830 · report
parse_ratios dongbeank/CATS/utils/tools.py official repository ran MIT (permissive) · cb391e8599bbce03 · report
CORR dongbeank/CATS/utils/metrics.py official repository unverified MIT (permissive) · 8c76ef42a9443f81 · report
Decoder dongbeank/cats/models/CATS.py official repository unverified MIT (permissive) · 1a7ac87e34d772f5 · report
Dummy_Embedding dongbeank/cats/models/CATS.py official repository unverified MIT (permissive) · 9362e3c7bb8cc5f2 · report
MAE dongbeank/CATS/utils/metrics.py official repository unverified MIT (permissive) · 75f44993b096bf76 · report
Model_backbone dongbeank/cats/models/CATS.py official repository unverified MIT (permissive) · d682a61115f944df · report
RSE dongbeank/CATS/utils/metrics.py official repository unverified MIT (permissive) · b40a11875ebd0cd2 · report
time_features dongbeank/CATS/utils/timefeatures.py official repository unverified MIT (permissive) · 9a5fcd4ebfc55d03 · report
time_features_from_frequency_str dongbeank/CATS/utils/timefeatures.py official repository unverified MIT (permissive) · f8544563682146e5 · report

Tasks

Time SeriesTime Series Forecasting

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Methods

Absolute Position EncodingsAdamAttentionBPEDense ConnectionsDropoutFocusLabel SmoothingLayer NormalizationLinear LayerMulti-Head AttentionPosition-Wise Feed-Forward LayerResidual ConnectionSoftmaxTransformer

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