TA-SparseMG: Trend-Aware Sparse Forecasting via Multi-Scale Gating for Long-Term Time Series
arXiv:2606.27908v1 Announce Type: new Abstract: Long-term time series forecasting finds extensive applications in domains such as power demand, traffic flow, meteorological observation, and renewable energy dispatch. Forecasting dynamically varying long-term time series poses inherent challenges, including statistical nonstationarity, local high-frequency disturbances, and coupled cross-period dependencies, which make it difficult for lightweight models to balance parameter efficiency and forecasting performance. To address this issue, this study presents TA-SparseMG, a lightweight cross-perio
The increasing complexity and scale of real-world time series data across critical infrastructure demands more efficient and accurate forecasting methods.
Improved long-term time series forecasting can lead to better resource management, operational efficiency, and predictive capabilities in crucial sectors like energy and logistics.
New lightweight models are emerging that can handle the challenges of long-term time series forecasting, offering a balance between computational efficiency and performance.
- · Energy Grid Operators
- · Logistics and Supply Chain Managers
- · Meteorological Services
- · AI/ML Developers
- · Inefficient Forecasting Models
- · Companies reliant on short-term predictions
More accurate predictions reduce waste and improve planning in energy and transportation sectors.
Reduced operational costs and increased resilience for critical infrastructure facing dynamic environments.
Enhanced predictive capabilities contribute to the broader adoption of AI agents for automated decision-making and optimization.
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Read at arXiv cs.LG