DrafTS: Time-Aware Decomposition with Residual Correction for Time Series Modeling

arXiv:2609.33368 · cs.AI · Submitted 2026-09-27 · Read on arXiv

cs.AI

Submitted: 2026-09-27

Updated: 2026-09-27

Code: https://github.com/Autumn61q/DrafTS

License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/

Terminology

• Time Series Forecasting — 12× in this paper · explained in 3 episodes
• Transformer — 12× in this paper · explained in 2 episodes
• Accuracy — 10× in this paper · explained in 1 episode
• Alignment — 7× in this paper · explained in 1 episode
• Ablation — 6× in this paper · explained in 1 episode
• Adam — 6× in this paper · explained in 2 episodes
• Benchmark — 6× in this paper · explained in 2 episodes
• Residual-Aware Correction — 5× in this paper
• Adaptivity — 3× in this paper · explained in 1 episode
• Robustness — 3× in this paper · explained in 5 episodes
• AUC — 2× in this paper · explained in 2 episodes
• Ablation study — 2× in this paper · explained in 1 episode
• Aggregation — 2× in this paper · explained in 1 episode
• Model-Agnostic — 2× in this paper · explained in 2 episodes
• Benchmarking — 1× in this paper · explained in 3 episodes
• Calibration — 1× in this paper · explained in 7 episodes
• Chaotic Dynamics — 1× in this paper · explained in 2 episodes
• Contrastive Learning — 1× in this paper · explained in 5 episodes
• F1 Score — 1× in this paper · explained in 3 episodes
• Foundation Model — 1× in this paper · explained in 3 episodes
• Foundation Models — 1× in this paper · explained in 7 episodes
• Generative AI — 1× in this paper · explained in 2 episodes
• Loss Function — 1× in this paper · explained in 2 episodes
• Mixture of Experts — 1× in this paper · explained in 2 episodes

Sources

• The UEA multivariate time series classification archive, 2018
• iTransformer: Inverted Transformers Are Effective for Time Series Forecasting
• A Time Series is Worth 64 Words: Long-term Forecasting with Transformers

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