Dual-Prototype Disentanglement: A Context-Aware Enhancement Framework for Time Series Forecasting
arXiv:2601.16632v4 Announce Type: replace Abstract: Time series forecasting has witnessed significant progress with deep learning. While prevailing approaches enhance forecasting performance by modifying architectures or introducing novel enhancement strategies, they often fail to dynamically disentangle and leverage the complex, intertwined temporal patterns inherent in time series, thus resulting in the learning of static, averaged representations that lack context-aware capabilities. To address this, we propose the Dual-Prototype Adaptive Disentanglement framework (DPAD), a model-agnostic a
The continuous advancements in deep learning necessitate more sophisticated methods to handle the complexity and dynamicity of real-world time series data, pushing for context-aware forecasting.
Improved time series forecasting directly impacts decision-making in various critical sectors by offering more accurate predictions for dynamic systems.
The proposed DPAD framework introduces a model-agnostic approach for dynamically disentangling temporal patterns, potentially offering more robust and adaptable forecasting models.
- · AI researchers
- · Deep learning practitioners
- · Industries relying on forecasting (e.g., finance, logistics, energy)
- · Static forecasting models
Enhanced accuracy and reliability in predictive analytics across multiple domains.
Faster and more efficient resource allocation or risk management based on superior forecasts.
Potentially enables new classes of autonomous systems or market strategies that depend on ultra-precise future state predictions.
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Read at arXiv cs.LG