Introduction to time series preprocessing and forecasting in Python using AR, MA, ARMA, ARIMA, SARIMA and Prophet model with forecast evaluation.

Material for the course "Time series analysis with Python"

Rapid large-scale fractional differencing with NVIDIA RAPIDS and GPU to minimize memory loss while making a time series stationary. 6x-400x speed up over CPU implementation.

Bitcoin price prediction using ARIMA Model.

Stationarity check using the Augmented Dickey-Fuller test from Scratch in Python

Forecast the Airlines Passengers. Prepare a document for each model explaining how many dummy variables you have created and RMSE value for each model. Finally which model you will use for Forecasting.

Resampling procedure for weakly dependent stationary observations.

Filters (kalman, hodrick-prescott, moving average) together with comparison and sensitivity analysis (in notebook filters_with_parameters)+var analysis and granger causality test. Test for random walk (CE currencies using yfinance API)

Resampling procedure for weakly dependent stationary observations.

'XTARIMAU': module to find the best [S]ARIMA[X] models in heterogeneous panels with the help of arimaauto

R finance guide - Algotrading101

Common vulnerabilities and exposure.

📈 Make your time series stationary automatically using Python

Time Series Analysis of Zillow data

Statistical tests of time series using python

'ARIMAAUTO': module to find the best ARIMA model with the help of a Stata-adjusted Hyndman-Khandakar (2008) algorithm

Stochastic simulations of population abundance with known component density feedback on survival to test for ability to return ensemble feedback signal

Machine Learning in Scikit-Learn and TensorFlow

This repo is about forecasting the Yen movements in order to know whether to be long or short.