Pinned Repositories
2021.0324
awc
Adaptive Weights Clustering
BayesMAR
Bayesian Median Autoregressive model for time series forecasting
Density-Review
DupireNN
Neural network local volatility with dupire formula
FAIRforecast
Automatic interpretable sales forecasting for R
fastai
The fastai deep learning library
GLR_ADV
Training artificial neural networks by generalized likelihood ratio method:exploring brain-like learning to improve adversarial defensiveness
LINDA_DSS
MLAlgorithms
Minimal and clean examples of machine learning algorithms implementations
mArtukhov's Repositories
mArtukhov/2021.0324
mArtukhov/awc
Adaptive Weights Clustering
mArtukhov/BayesMAR
Bayesian Median Autoregressive model for time series forecasting
mArtukhov/Density-Review
mArtukhov/DupireNN
Neural network local volatility with dupire formula
mArtukhov/FAIRforecast
Automatic interpretable sales forecasting for R
mArtukhov/fastai
The fastai deep learning library
mArtukhov/GLR_ADV
Training artificial neural networks by generalized likelihood ratio method:exploring brain-like learning to improve adversarial defensiveness
mArtukhov/LINDA_DSS
mArtukhov/MLAlgorithms
Minimal and clean examples of machine learning algorithms implementations
mArtukhov/MultiTransformer
Transformer and MultiTransformer layers for stock volatility forecasting purposes
mArtukhov/pyalgotrade
Python Algorithmic Trading Library
mArtukhov/Pyrgos
Репозиторий содержит коды программного комплекса "Пиргос" (разработан в при поддержке РФФИ в рамках проекта 15-32-01390), направленного на регистрацию в ФГБУ «Федеральный институт промышленной собственности», заявление № 252062129. The repository contains the code and libraries for Pyrgos solution for market calibration, risk management and option pricing. The code and algorithms provided are invented during the research supported by RFBR grant (project 15-32-01390)
mArtukhov/scalable-data-driven-assortment-planning
Code accompanying paper titled "Optimizing Revenue over Data-driven Assortments" (2017)
mArtukhov/SETAR_Trees
This repository contains the experiments related to a new and accurate tree-based global forecasting algorithm named, SETAR-Tree.