Research
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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

Trend · papers per month

count% of corpus
1122 · Mar 201919922001200920172026
11 results for “Saudi Arabia”
Modeling High-Resolution Spatio-Temporal Wind with Deep Echo State Networks and Stochastic Partial Differential Equationsstat.ML

Modeling wind dynamics in Saudi Arabia using deep learning and stochastic PDEs.

problem Accurately modeling spatio-temporal wind patterns in a large, diverse, and understudied region.
method Energy distance-based spatial reduction, sparse stochastic Echo State Network, non-stationary stochastic PDE reconstruction.
result Produces more accurate wind speed and energy forecasts, saving $1 million annually.
wind modelingSaudi Arabiadeep learningstochastic PDEenergy forecasting
2024-12-10abs ↗pdf ↗
The evolving of Data Science and the Saudi Arabia case. How much have we changed in 13 years?stat.AP

This study analyzes data science vocabulary changes over 13 years.

problem Understanding evolution of data science terms over time.
method Exploratory Data Analysis, Latent Semantic Analysis, Latent Dirichlet Analysis, N-grams Analysis.
result Identified new vocabulary and its incorporation into scientific literature.
data sciencevocabulary evolutionExploratory Data AnalysisLatent Semantic AnalysisLatent Dirichlet Analysis
2023-10-23abs ↗pdf ↗
CESAR: A Convolutional Echo State AutoencodeR for High-Resolution Wind Forecastingstat.AP

CESAR improves wind speed and power forecasting for high-resolution simulations.

problem Accurate high-resolution wind forecasting for efficient power grid management.
method A spatio-temporal neural network model using deep convolutional autoencoder and echo state network.
result CESAR provides up to 17% improvement in wind speed and power forecasting compared to best alternatives.
wind forecastingneural networksecho state networksdeep learningspatial-temporal modeling
2024-12-13abs ↗pdf ↗
Neural Bayes estimators for censored inference with peaks-over-threshold modelsstat.ME

Efficient neural Bayes estimators for censored peaks-over-threshold models improve inference speed and accuracy.

problem Computational burden in inference with spatial extremal dependence models due to intractable or censored likelihoods.
method Developed neural Bayes estimators using data augmentation techniques to encode censoring information.
result Significant gains in computational and statistical efficiency compared to traditional methods.
neural Bayes estimatorscensored peaks-over-threshold modelslikelihood-free inferenceextremal dependence modelsdata augmentation
2023-06-27abs ↗pdf ↗
Multifractal wavelet dynamic mode decomposition modeling for marketing time seriesq-fin.MF

Study combines dynamic mode and wavelet decomposition for marketing time series analysis.

problem Insufficient quantitative studies in marketing literature.
method Dynamic mode decomposition and wavelet decomposition for marketing time series.
result Effect of time scale on brand sales persistence and forecasting.
marketingtime seriesdynamic mode decompositionwavelet decompositionbrand sales
2024-03-20abs ↗pdf ↗
Cohomology of generalized configuration spacesmath.AT

Let XX be a topological space. We consider certain generalized configuration spaces of points on XX, obtained from the cartesian product XnX^n by removing some intersections of diagonals. We give a systematic framework for studying the cohomology of such spaces using what we call "tcdga models" for the cochains on $X…

2018-07-19abs ↗pdf ↗