On-device research index

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.

168,657 papers · 148 categories

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11 results for “N-BEATS”

N-BEATS-MOE: N-BEATS with a Mixture-of-Experts Layer for Heterogeneous Time Series Forecastingcs.LG

N-BEATS-MOE improves time series forecasting by adapting to series characteristics.

problem Forecasting heterogeneous time series with varying characteristics.

method Mixture-of-Experts layer with dynamic block weighting.

result Consistent improvements across 12 benchmark datasets, especially for heterogeneous series.

time series forecasting deep learning Mixture-of-Experts dynamic block weighting heterogeneous series

2025-08-10abs ↗pdf ↗

Advancing Financial Forecasting: A Comparative Analysis of Neural Forecasting Models N-HiTS and N-BEATSq-fin.CP

This study compares two neural models for financial forecasting, showing their superiority.

problem Improving financial market trend predictions using neural networks.

method Systematic comparison of N-HiTS and N-BEATS with conventional models.

result N-HiTS and N-BEATS enhance forecast accuracy and robustness in financial time series data.

financial forecasting neural networks N-HiTS N-BEATS non-linear dynamics

2024-08-31abs ↗pdf ↗

Neural forecasting at scalecs.LG

N-BEATS(P) efficiently forecasts millions of time series with reduced memory and time.

problem Efficiently forecasting millions of time series with high accuracy.

method Global parallel variant of N-BEATS model designed for multi-step time series forecasting.

result Significant reduction in training time and memory usage with comparable accuracy.

time series forecasting deep learning ensemble models neural networks parallel computing

2021-09-20abs ↗pdf ↗

Topological Attention for Time Series Forecastingcs.LG

Topological attention improves forecasting of univariate time series.

problem Forecasting univariate time series using local topological features.

method Topological attention mechanism that integrates local topological properties into forecasting models.

result Topological attention leads to state-of-the-art performance on the M4 benchmark.

time series forecasting topological attention persistent homology neural networks N-BEATS

2021-07-19abs ↗pdf ↗

Using dynamic loss weighting to boost improvements in forecast stabilitycs.LG

This paper improves forecast stability without sacrificing accuracy using dynamic loss weighting.

problem Rolling origin forecast instability in time series forecasting.

method Dynamic loss weighting algorithms applied to the N-BEATS model.

result Dynamic loss weighting can further improve forecast stability without compromising accuracy.

forecast stability dynamic loss weighting time series forecasting N-BEATS forecast instability

2024-09-26abs ↗pdf ↗

N-BEATS: Neural basis expansion analysis for interpretable time series forecastingcs.LG

We focus on solving the univariate times series point forecasting problem using deep learning. We propose a deep neural architecture based on backward and forward residual links and a very deep stack of fully-connected layers. The architecture has a number of desirable properties, being interpretable, applicable withou…

2019-05-24abs ↗pdf ↗

Forecasting Emergency Department Crowding with Advanced Machine Learning Models and Multivariable Inputcs.LG

Study improves forecasting of ED crowding using advanced ML models.

problem Improving forecasting of emergency department crowding.

method Advanced machine learning models (N-BEATS, LightGBM, DeepAR) using multivariable input data.

result N-BEATS and LightGBM outperform benchmarks in forecasting ED occupancy.

emergency department crowding machine learning forecasting multivariable input

2023-08-31abs ↗pdf ↗

Forecasting Algorithms for Causal Inference with Panel Dataecon.EM

Improved causal inference with panel data using deep learning.

problem Causal inference challenges in social science with panel data.

method Adapted N-BEATS deep neural architecture for time series forecasting.

result SyNBEATS estimator outperforms existing methods in panel data settings.

causal inference panel data deep learning forecasting counterfactual

2022-08-06abs ↗pdf ↗

Heads, Not Backbones: Output Heads Dominate Architectures on Fat-Tailed Returnscs.LG

Deep forecasting models show output heads significantly improve performance on fat-tailed financial returns.

problem Improving deep learning models for forecasting fat-tailed financial returns.

method Comparison of backbone architectures and output heads (point, Gaussian, Gaussian mixture) on S&P 500 monthly log-returns.

result Switching from point to Gaussian heads improves CRPS by about 1.3 percent, and from Gaussian to mixture adds another 2.4 percent.

deep learning financial forecasting fat-tailed returns CRPS Gaussian mixture

2026-06-29abs ↗pdf ↗

Towards local testability for quantum codingquant-ph

We introduce the hemicubic codes, a family of quantum codes obtained by associating qubits with the $p$ -faces of the $n$ -cube (for $n>p$ ) and stabilizer constraints with faces of dimension $(p\pm1)$ . The quantum code obtained by identifying antipodal faces of the resulting complex encodes one logical qubit into $N = 2^…

2019-11-08abs ↗pdf ↗

Hierarchical forecasting with a top-down alignment of independent level forecastsstat.ML

A new hierarchical forecasting method improves overall accuracy.

problem Hierarchical forecasting challenges, especially for intermittent time series.

method Top-down alignment of independent level forecasts using deep learning and tree-based algorithms.

result Improves overall forecasting accuracy compared to existing methods.

forecasting hierarchical deep learning tree-based alignment

2021-03-15abs ↗pdf ↗