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Deep learning estimates time-varying parameters for AR(p) forecasting

By PulseAugur Editorial · [2 sources] ·

Researchers have developed a novel forecasting framework utilizing a deep learning approach to estimate time-dependent parameters in AR(p) processes. This method allows for the capture of complex, nonstationary patterns while maintaining a clear parametric structure. The framework is designed to handle both Gaussian and Laplace-distributed noise, offering robust uncertainty quantification and prediction intervals for various forecasting scenarios. AI

IMPACT This research could lead to more accurate and flexible forecasting tools for complex, nonstationary data across various domains.

RANK_REASON The cluster contains an academic paper detailing a new methodology for time-series forecasting using deep learning. [lever_c_demoted from research: ic=1 ai=1.0]

Read on arXiv cs.LG →

AI-generated summary · Google Gemini · from 2 sources. How we write summaries →

Deep learning estimates time-varying parameters for AR(p) forecasting

COVERAGE [2]

  1. arXiv cs.LG TIER_1 English(EN) · Martyna Wiącek ·

    Neural Network-Based Estimation of Time-Dependent Parameters in AR(p) Processes

    We investigate a forecasting framework based on a simple discrete-time dynamic model with coefficients varying in time. The parameters of the model are recovered within a deep learning framework, which makes it possible to retain a transparent parametric structure while simultane…

  2. arXiv stat.ML TIER_1 English(EN) · Agnieszka Kope\'c, Pawe{\l} Przyby{\l}owicz, Martyna Wi\k{a}cek ·

    Neural Network-Based Estimation of Time-Dependent Parameters in AR(p) Processes

    arXiv:2607.00470v1 Announce Type: new Abstract: We investigate a forecasting framework based on a simple discrete-time dynamic model with coefficients varying in time. The parameters of the model are recovered within a deep learning framework, which makes it possible to retain a …

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