dorsal/arxiv
View SchemaDDT: A Dual-Masking Dual-Expert Transformer for Energy Time-Series Forecasting
| Authors | Mingnan Zhu, Qixuan Zhang, Yixuan Cheng, Fangzhou Gu, Shiming Lin |
|---|---|
| Categories | |
| ArXiv ID | 2601.07250vv1 |
| URL | https://arxiv.org/abs/2601.07250 |
| License | http://arxiv.org/licenses/nonexclusive-distrib/1.0/ |
Abstract
Accurate energy time-series forecasting is crucial for ensuring grid stability and promoting the integration of renewable energy, yet it faces significant challenges from complex temporal dependencies and the heterogeneity of multi-source data. To address these issues, we propose DDT, a novel and robust deep learning framework for high-precision time-series forecasting. At its core, DDT introduces two key innovations. First, we design a dual-masking mechanism that synergistically combines a strict causal mask with a data-driven dynamic mask. This novel design ensures theoretical causal consistency while adaptively focusing on the most salient historical information, overcoming the rigidity of traditional masking techniques. Second, our architecture features a dual-expert system that decouples the modeling of temporal dynamics and cross-variable correlations into parallel, specialized pathways, which are then intelligently integrated through a dynamic gated fusion module. We conducted extensive experiments on 7 challenging energy benchmark datasets, including ETTh, Electricity, and Solar. The results demonstrate that DDT consistently outperforms strong state-of-the-art baselines across all prediction horizons, establishing a new benchmark for the task.
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"abstract": "Accurate energy time-series forecasting is crucial for ensuring grid stability and promoting the integration of renewable energy, yet it faces significant challenges from complex temporal dependencies and the heterogeneity of multi-source data. To address these issues, we propose DDT, a novel and robust deep learning framework for high-precision time-series forecasting. At its core, DDT introduces two key innovations. First, we design a dual-masking mechanism that synergistically combines a strict causal mask with a data-driven dynamic mask. This novel design ensures theoretical causal consistency while adaptively focusing on the most salient historical information, overcoming the rigidity of traditional masking techniques. Second, our architecture features a dual-expert system that decouples the modeling of temporal dynamics and cross-variable correlations into parallel, specialized pathways, which are then intelligently integrated through a dynamic gated fusion module. We conducted extensive experiments on 7 challenging energy benchmark datasets, including ETTh, Electricity, and Solar. The results demonstrate that DDT consistently outperforms strong state-of-the-art baselines across all prediction horizons, establishing a new benchmark for the task.",
"arxiv_id": "2601.07250",
"authors": [
"Mingnan Zhu",
"Qixuan Zhang",
"Yixuan Cheng",
"Fangzhou Gu",
"Shiming Lin"
],
"categories": [
"cs.LG",
"cs.AI"
],
"license": "http://arxiv.org/licenses/nonexclusive-distrib/1.0/",
"title": "DDT: A Dual-Masking Dual-Expert Transformer for Energy Time-Series Forecasting",
"url": "https://arxiv.org/abs/2601.07250",
"version": "v1"
},
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"variant": "snapshot-2026-01-17",
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