arXiv:2605.11287v2 Announce Type: replace-cross Abstract: A persistent paradox in time-series forecasting is that structurally simple MLP and linear models often outperform high-capacity Transformers. We argue that this gap arises from a mismatch in the sequence-modeling primitive: while many time-series dynamics are governed by global temporal operators (e.g., filtering and harmonic structure), standard attention forms each output as a convex combination of inputs. This restricts its ability to represent signed and oscillatory transformations that are fundamental to temporal signal processing
This research addresses a persistent limitation of Transformers in time-series forecasting, a critical area for AI applications, indicating a current push to refine AI models for specialized data types.
Improving time-series analysis directly impacts industries reliant on predictive analytics, from finance to operational logistics, and contributes to the broader efficacy of AI models.
The proposed 'Temporal Operator Attention' suggests a new architectural primitive that could significantly enhance the performance of AI models on time-series data, moving beyond the limitations of standard attention mechanisms.
- · AI researchers and developers
- · Companies using time-series forecasting
- · Predictive analytics platforms
- · Sectors with complex temporal data
- · AI models reliant solely on standard attention for time-series
- · Systems with suboptimal time-series forecasting accuracy
Improved accuracy in demand forecasting, anomaly detection, and predictive maintenance across various industries.
Accelerated development of more robust AI agents and autonomous systems that require nuanced understanding of temporal dynamics.
Enhanced AI capabilities leading to the automation of more complex operational and strategic decision-making processes.
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Read at arXiv cs.AI