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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.

169,051 papers · 148 categories

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6 results for “GBRT”

Do We Really Need Deep Learning Models for Time Series Forecasting?cs.LG

Simple GBRT model improved by window-based input transformation outperforms state-of-the-art deep learning models.

problem Improving performance of traditional forecasting models for time series data.

method Transformed GBRT model input structure to include target values and external features, forming one input instance per training window.

result Simple GBRT model with window-based input transformation outperformed state-of-the-art deep learning models on nine datasets.

time series forecasting GBRT model deep learning window-based input state-of-the-art models

2021-01-06abs ↗pdf ↗

Predicting wind pressures around circular cylinders using machine learning techniquescs.LG

Machine learning predicts wind pressures around circular cylinders efficiently.

problem Predicting wind pressures around circular cylinders using traditional methods is costly and time-consuming.

method Trained GBRT models using Reynolds number, turbulence intensity, and circumferential angle as inputs.

result GBRT models accurately predict wind pressures for a wide range of Reynolds and turbulence intensities.

wind pressure circular cylinder machine learning GBRT Reynolds number

2019-01-21abs ↗pdf ↗

Gradient Regularized Budgeted Boostingcs.LG

Proposes an algorithm for semi-supervised learning with budget constraints.

problem Learning classifiers within test-time budget constraints with limited labeled data.

method Leverages unlabeled data through Laplace smoothing and gradient boosted regression trees.

result First algorithm for semi-supervised budgeted learning.

semi-supervised learning budget constraints gradient boosting Laplace smoothing regression trees

2019-01-13abs ↗pdf ↗

Predicting Soil pH by Using Nearest Fieldscs.LG

The paper proposes a soil pH prediction method using nearest fields.

problem Expensive soil sampling and testing in precision agriculture.

method Spatial radius queries and regression techniques in data mining.

result Predicted soil pH values achieved high accuracy (R_2 values of 0.718 and MAE values of 0.29).

soil pH precision agriculture nearest fields spatial queries regression

2019-12-03abs ↗pdf ↗

Gradient Boosted Binary Histogram Ensemble for Large-scale Regressionstat.ML

Proposes GBBHE for efficient large-scale regression.

problem Large-scale regression problems.

method Gradient Boosting with binary histogram partition and ensemble learning.

result Improves computational efficiency and performance on large datasets.

Gradient Boosting Histogram Partition Large-scale Regression Statistical Convergence Ensemble Learning

2021-06-03abs ↗pdf ↗

Sequence to sequence deep learning models for solar irradiation forecastingcs.LG

Deep learning models predict solar irradiance for short-term forecasts.

problem Accurate prediction of solar irradiance for renewable energy integration.

method Sequence-to-sequence LSTM models for GHI forecasting, incorporating spatial-temporal features.

result LSTM models outperform traditional techniques in short-term GHI forecasting.

solar irradiance deep learning sequence-to-sequence LSTM weather forecasting

2019-04-30abs ↗pdf ↗