arXiv:2605.21088v1 Announce Type: new Abstract: Modern deep-learning models have achieved remarkable success in time-series forecasting. Yet, their performance degrades in long-term prediction due to error accumulation in autoregressive inference, where predictions are recursively used as inputs. While classical error correction mechanisms (ECMs) have long been used in statistical methods, their applicability to deep learning models remains limited or ineffective. In this work, we revisit the error accumulation problem in deep time-series forecasting and investigate the role and necessity of E
The paper addresses a known limitation in current deep learning models for time-series forecasting, specifically error accumulation, a problem that becomes more critical as deep learning applications expand into areas requiring long-term predictions.
Improved accuracy in long-term time-series forecasting will enhance reliability across various predictive applications, from financial models and supply chain logistics to climate predictions and resource management, impacting planning and operational efficiency.
The re-introduction and adaptation of classical error correction mechanisms into modern deep learning architectures could lead to more robust and accurate predictions for longer horizons, enabling better decision-making in complex systems.
- · AI/ML researchers
- · Deep learning practitioners
- · Industries relying on time-series forecasting
- · Data scientists
Enhancements in time-series forecasting accuracy for deep learning models.
Increased adoption of deep learning for critical long-term prediction tasks previously limited by error accumulation.
Optimization of resource allocation and strategic planning across various sectors due to more reliable future outlooks.
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Read at arXiv cs.LG