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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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8 results for “NO2”

Sparse Generalized Yule-Walker Estimation for Large Spatio-temporal Autoregressions with an Application to NO2 Satellite Dataecon.EM

Estimates spatio-temporal data with satellite NO2 concentrations using Yule-Walker equations.

problem Estimating large spatio-temporal autoregressions with unknown spatial interactions.

method Sparse generalized Yule-Walker estimation, penalized regression, spatial and temporal dependence.

result Strong forecast improvements and evidence of spatial interactions in NO2 satellite data.

spatio-temporal autoregression Yule-Walker equations spatial interactions satellite NO2 forecast improvement

2021-08-05abs ↗pdf ↗

Probabilistic forecasting approaches for extreme NO

_2

episodes: a comparison of modelsstat.AP

Quantile gradient boosted trees outperform other models in predicting NO2 concentration distributions.

problem Forecasting high NO2 concentration episodes for effective air quality management.

method Compared 10 probabilistic forecasting models for NO2 concentration prediction.

result Quantile gradient boosted trees model outperformed others in predicting NO2 concentration distributions.

NO2 forecasting probabilistic gradient boosted trees air quality

2020-03-24abs ↗pdf ↗

Dynamic Principal Component Analysis: Identifying the Relationship between Multiple Air Pollutantsstat.AP

The dynamic nature of air quality chemistry and transport makes it difficult to identify the mixture of air pollutants for a region. In this study of air quality in the Houston metropolitan area we apply dynamic principal component analysis (DPCA) to a normalized multivariate time series of daily concentration measurem…

2016-08-10abs ↗pdf ↗

PlumeNet: Large-Scale Air Quality Forecasting Using A Convolutional LSTM Networkcs.LG

Engine forecasts NO2, O3, PM2.5, PM10 with high accuracy.

problem Accurate long-term air quality forecasting.

method Convolutional LSTM network trained on grid data.

result 4-day forecasts significantly outperform simple benchmarks.

air quality forecasting convolutional LSTM long-term prediction neural network weather forecasting

2020-06-14abs ↗pdf ↗

MapLUR: Exploring a new Paradigm for Estimating Air Pollution using Deep Learning on Map Imagescs.LG

MapLUR uses deep learning on map images to estimate NO2 pollution, outperforming traditional methods.

problem Limited availability of data for traditional LUR models makes them hard to adapt to new areas.

method Data-driven, open-source approach using convolutional neural networks trained on map data.

result MapLUR significantly outperforms traditional LUR models, including those with manually engineered features.

deep learning air pollution convolutional neural networks map data land-use regression

2020-02-18abs ↗pdf ↗

Coping with area price risk in electricity markets: Forecasting Contracts for Difference in the Nordic power marketstat.AP

Contracts for Difference (CfDs) are forwards on the spread between an area price and the system price. Together with the system price forwards, these products are used to hedge the area price risk in the Nordic electricity market. The CfDs are typically available for the next two months, three quarters and three years.…

2014-06-26abs ↗pdf ↗

DeepPlume: Very High Resolution Real-Time Air Quality Mappingcs.LG

Engine predicts real-time air quality with high resolution.

problem Real-time prediction of air pollutants for health monitoring.

method Combines official data, models, land cover, traffic data for high-resolution predictions.

result Engine produces predictions with resolution of a few dozen meters.

air quality real-time high resolution nitrogen dioxide ozone

2020-02-14abs ↗pdf ↗

Low-Dimensional High-Fidelity Kinetic Models for NOX Formation by a Compute Intensification Methodphysics.chem-ph

Compact models for NOX formation during methane combustion are created using a new algorithm.

problem Creating accurate models for NOX formation during complex combustion processes.

method Adapted Machine Learning Optimization of Chemical Kinetics (MLOCK) algorithm with Latin Square method for virtual reaction network generation.

result Compact models with high fidelity (>75%) in reproducing industry-defined performance targets are generated.

NOX formation kinetic models machine learning methane combustion compact models

2022-02-21abs ↗pdf ↗