vchung18

vchung18's Stars

• Pyomo/pyomo

  An object-oriented algebraic modeling language in Python for structured optimization problems.

  Language:Python1.9k495

• herbermqh/BYUTextbook

  Plantilla LaTeX

  Language:TeX52

• mfarragher/appelpy

  Applied Econometrics Library for Python

  Language:Python16815

• lbraglia/RStata

  [R package]: A R-Stata interface

  Language:R10536

• Nixtla/neuralforecast

  Scalable and user friendly neural :brain: forecasting algorithms.

  Language:Python2.8k320

• TomasBeuzen/machine-learning-tutorials

  A collection of tutorials for different machine learning tasks

  Language:Jupyter Notebook4313

• unit8co/darts

  A python library for user-friendly forecasting and anomaly detection on time series.

  Language:Python7.6k831

• sktime/sktime

  A unified framework for machine learning with time series

  Language:Python7.6k1.3k

• herbermqh/BMC_tex

  My bespoke, multipurpose class; designed for general use in LaTeX documents.

  31

• JoaquinAmatRodrigo/skforecast

  Time series forecasting with machine learning models

  Language:Jupyter Notebook1k116

• zgana/fpp3-python-readalong

  Python-centered read-along of Forecasting: Principles and Practice

  Language:Jupyter Notebook447146

• qs66/An-Alternative-Approach-to-Forecast-the-Volatility-of-Multiscale-and-High-Dimensional-Market-Data

  Traditional methods for volatility forecast of multiscale and high-dimensional data like foreign-exchange and stock market volatility have both advantages and disadvantages which have been identified. In my project, I apply the Support Vector Machine (SVM) as a complimentary volatility method which is capable dealing of such type of data. SVM-based models may extract extra information of time series data and handle the long memory effect very well. Our Support Vector Machine for Regression (SVR) model has better result than the common GARCH (1, 1) model. The predictions are closer to the historical data and the error is lower. In addition, I test different kernels to see the performance difference. For my data, rbf kernel has an overall better performance than linear and polynomial kernels. I conclude that SVM-based model may be applied more frequently in the emerging field of high-frequency finance and in multivariate models for portfolio risk management.

  Language:Python114

• herbermqh/tex-master-thesis-template

  A template for writing a nice master thesis dissertation with LaTeX

  51

• bcbarsness/machine-learning

  Language:Jupyter Notebook525

• MiyainNYC/Financial-Modeling

  Option Pricing, Volatility Prediction, Machine Learning, Black Scholas, Web Crawling

  Language:Jupyter Notebook5124

• PacktPublishing/LaTeX-Beginner-s-Guide-Second-Edition

  LaTeX Beginner's Guide - Second Edition, published by Packt

  Language:TeX9346

• lukas-blecher/LaTeX-OCR

  pix2tex: Using a ViT to convert images of equations into LaTeX code.

  Language:Python11.5k953

• James-Yu/LaTeX-Workshop

  Boost LaTeX typesetting efficiency with preview, compile, autocomplete, colorize, and more.

  Language:TypeScript10.4k519

• jankapunkt/latexcv

  :necktie: A collection of cv and resume templates written in LaTeX. Leave an issue if your language is not supported!

  Language:TeX2.8k575

• herbermqh/nexusTeX

  A style file for LaTeX

  Language:TeX3

• IRkernel/IRkernel

  R kernel for Jupyter

  Language:Jupyter Notebook1.6k290

• ccolonescu/PoEdata

  R data sets for "Principles of Econometrics" by Hill, Griffiths, and Lim, 4e, Wiley

  Language:R4238

• JustinMShea/wooldridge

  The official R data package for "Introductory Econometrics: A Modern Approach". A vignette contains example models from each chapter.

  Language:R19370

• woerman/ResEcon703

  Topics in Advanced Econometrics (ResEcon 703). University of Massachusetts Amherst. Taught by Matt Woerman

  Language:TeX203112

• davidrpugh/econometrics-labs

  Graduate level econometrics labs in Python/R

  Language:Python4333

• mca91/EconometricsWithR

  📖An interactive companion to the well-received textbook 'Introduction to Econometrics' by Stock & Watson (2015)

  Language:R465268

• microsoft/ML-For-Beginners

  12 weeks, 26 lessons, 52 quizzes, classic Machine Learning for all

  Language:HTML68.4k14k

• csatzky/forecasting-realized-volatility-using-supervised-learning

  Traditionally, volatility is modeled using parametric models. This project focuses on predicting EUR/USD volatility using more flexible, machine-learning methods.

  Language:R215

• PacktPublishing/Machine-Learning-for-Time-Series-with-Python

  Machine Learning for Time-Series with Python.Published by Packt

  Language:Jupyter Notebook203138

• iankhr/armagarch

  ARMA-GARCH

  Language:Python8927