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Time series models need long context windows for process identification

By PulseAugur Editorial · [1 sources] ·

Researchers have investigated the necessity of long context windows for time series forecasting models. They propose that beyond capturing long-range dependencies, these windows aid in identifying the specific data-generating process. The study demonstrates that larger context windows reduce uncertainty about the underlying process, which is crucial for accurate predictions. The findings suggest that decoupling process identification from conditional forecasting can enhance scalability without sacrificing accuracy. AI

IMPACT Provides theoretical grounding for the design of more effective time series forecasting architectures.

RANK_REASON Academic paper detailing research findings on time series models. [lever_c_demoted from research: ic=1 ai=1.0]

Read on arXiv cs.AI →

AI-generated summary · Google Gemini · from 1 sources. How we write summaries →

Time series models need long context windows for process identification

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Academic paper detailing research findings on time series models. [lever_c_demoted from research: ic=1 ai=1.0]
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143 days old
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COVERAGE [1]

  1. arXiv cs.AI TIER_1 English(EN) · Luca Butera, Giovanni De Felice, Andrea Cini, Cesare Alippi ·

    Why Do Time Series Models Need Long Context Windows?

    arXiv:2606.01999v1 Announce Type: cross Abstract: Modern deep learning models for forecasting groups of time series rely on increasingly longer observation windows. However, the benefit of increasing the window size is often simply attributed to capturing long-range dependencies,…

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