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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4 results for “NeuralProphet”

NeuralProphet: Explainable Forecasting at Scalecs.LG

NeuralProphet improves forecast accuracy by 55-92% for short-term forecasts.

problem Challenges in explainable, scalable forecasting for business decisions.

method Hybrid framework combining classical methods and deep learning, with auto-regression and covariate modules.

result NeuralProphet outperforms Prophet on real-world datasets and short-term forecasts.

forecasting explainable scalable NeuralProphet time series

2021-11-29abs ↗pdf ↗

Beyond the Hype: Comparing Lightweight and Deep Learning Models for Air Quality Forecastingcs.LG

Interpretable additive models outperform complex DL and hybrid pipelines for air quality forecasting.

problem Accurate forecasting of urban air pollution for public health and policy guidance.

method Investigated lightweight additive models (FBP, NP) vs. deep learning and hybrid pipelines on Beijing PM2.5 and PM10 data.

result Facebook Prophet consistently outperformed NeuralProphet and traditional models, achieving high

R^2

values.

air quality forecasting lightweight models Facebook Prophet NeuralProphet SARIMAX

2025-12-09abs ↗pdf ↗

Boosted Embeddings for Time Series Forecastingcs.LG

Boosted embeddings improve time series forecasting models.

problem Improving time series forecasting accuracy.

method Gradient boosting with DNN weak learners and incremental weight updates.

result DeepGB model outperforms state-of-the-art models.

time series forecasting gradient boosting deep learning embedding architecture sensor data

2021-04-10abs ↗pdf ↗

Curve Your Enthusiasm: Concurvity Regularization in Differentiable Generalized Additive Modelscs.LG

Regularizes GAMs to improve interpretability by reducing concurvity.

problem Susceptibility of GAMs to concurvity reduces interpretability.

method Proposes a regularizer to penalize pairwise correlations of non-linearly transformed features.

result Improves interpretability and reduces concurvity without sacrificing prediction quality.

concurvity interpretability regularization GAMs non-linear transformations

2023-05-19abs ↗pdf ↗