sadrzadehsaeed Goto Github PK

2018-hindsight

A repository containing the source code for the paper "HINDSIGHT: An R-Based Framework Towards Long Short Term Memory (LSTM) Optimization"

a-hybrid-deep-learning-based-model-for-volatility-prediction

Developing hybrid deep learning models by integrating Neural networks with (s,e,t)GARCH models to predict volatility in the Indian Commodity Market. We evaluate the following DNN models: Multi-layer Perceptron (MLP), Convolutional Neural Network (CNN), and RNN with Long-Short Term Memory (LSTM-RNN) and RNN with Gated-Recurrent Unit (GRU-RNN).

arima-lstm-hybrid-exchange-rate-forecast

bitcoin-prediction

bitcoin prediction algorithms

bitcoin-price-prediction-using-arima

Bitcoin price prediction using ARIMA Model.

bitcoin-volatility-analysis

Term Paper on Financial Econometrics exploring the volatility in Bitcoin prices as measured using a bayesian stochastic volatility analysis and using GARCH and rolling window GARCH estimates. These methods are compared and tested for the best way forward.

bitcoin_volatility_forecasting

GARCH and Multivariate LSTM forecasting models for Bitcoin realized volatility with potential applications in crypto options trading, hedging, portfolio management, and risk management

ceemdan-lstm

Paper-Reproduce: (ESWA) Forecasting the realized volatility of stock price index: A hybrid model integrating CEEMDAN and LSTM

economics_note

Computational material for Economics courses at UVic

fin-econ-project-bitcoin

forecast_cryptocurrencies_volatility_garch_variants

Understand and Model Cryptocurrencies Volatility Using GARCH Variants

historicalvolatility

A framework for historical volatility estimation and analysis.

lstmgarch_project

markov-switching-crypto-portfolio

Crypto Trading Strategy based on Markov Switching GARCH

midas

Using MIDAS Models on Russian Data. Cource work 2016

midas-gdp-forecast

My senior year project. Forecasting Turkey's real GDP growth using mixed data sampling regression model.

midaspy

Mixed Data Sampling (MIDAS) Modeling in Python

midaspy2

Python version of Mixed Data Sampling (MIDAS) regression

midasr

R package for mixed frequency time series data analysis.

quant-finance

Open souce quantitative finance models and algorithms with tutorials

r.mfiv

Model free implied volatility in R

rf-lstm-ceemdan

Building energy consumption prediction using hybrid RF-LSTM based CEEMDAN method

rumidas

:exclamation: This is a read-only mirror of the CRAN R package repository. rumidas — Univariate GARCH-MIDAS, Double-Asymmetric GARCH-MIDAS and MEM-MIDAS

sadrzadehsaeed

Config files for my GitHub profile.

skills-introduction-to-github

My clone repository

statistical-analysis-and-forecasting-of-crypto-tickers-for-crypto-treading

The goal of this study is to predict prices for Cryptocurrencies using Time series analysis and machine learning techniques. The purpose of this project is to take a sneak peek into the future by forecasting the next 30 days' average daily Realized Volatility (RV) of ETH-BTC using 2 different approaches - the traditional econometric approach to vol

the-comparison-of-some-algorithm-based-on-ceemdan

summer research

timeseriesforecastinginpython