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arXiv research

A locally-built, LLM-digested index of recent arXiv papers in quant finance, geometry/topology, and statistical ML — keyword search served straight from SQLite on this machine.

168,695 papers · 148 categories

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4 results for “NBEATS”

DMIDAS: Deep Mixed Data Sampling Regression for Long Multi-Horizon Time Series Forecastingcs.LG

DMIDAS improves long-term forecasting accuracy in healthcare and electricity data.

problem Challenging long-term forecasting accuracy and computational complexity.

method Smoothness regularization and mixed data sampling techniques integrated into NBEATS architecture.

result Improves prediction accuracy by 5% on long forecasting horizons (1000 timestamps) compared to state-of-the-art models.

long-term forecasting smoothness regularization mixed data sampling NBEATS healthcare

2021-06-07abs ↗pdf ↗

Neural basis expansion analysis with exogenous variables: Forecasting electricity prices with NBEATSxcs.LG

Improved electricity price forecasting with NBEATSx model.

problem Forecasting electricity prices with limited data.

method Extended NBEATS model to include exogenous variables.

result Significantly improved forecast accuracy (up to 5%) compared to existing methods.

electricity price forecasting NBEATSx deep learning exogenous variables time series analysis

2021-04-12abs ↗pdf ↗

Probabilistic AutoRegressive Neural Networks for Accurate Long-range Forecastingstat.ML

PARNN improves ARNN with ARIMA feedback for accurate long-range forecasting.

problem Accurate long-range forecasting of complex time series data.

method Improves ARNN using ARIMA feedback, providing uncertainty quantification.

result PARNN outperforms state-of-the-art forecasters across various horizons.

forecasting neural networks time series ARIMA uncertainty quantification

2022-04-01abs ↗pdf ↗

Pretrained Time-Series Foundation Models for Financial Return Forecastingq-fin.MF

Pretrained time-series models outperform train-from-scratch baselines in financial return forecasting.

problem Financial return forecasting

method Pretrained time-series foundation models

result Pretrained TSFMs dominate the ranking distribution, accounting for 8 of 10 task-level wins.

financial return forecasting pretrained models time-series foundation models TSFMs financial markets

2026-06-25abs ↗pdf ↗