stationarity

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

Book and material for the course "Time series analysis with Python" (STA-2003)

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.

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)

R finance guide - Algotrading101

Stationarity Tests in one place

Resampling procedure for weakly dependent stationary observations.

Resampling procedure for weakly dependent stationary observations.

Explains how to use ARIMA model to forecast future production units, enabling informed decision-making and planning in the electric and gas utilities sector.

Exploratory data analysis of CVE vulnerability data: time series, clustering, topic modelling.

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

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

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

An exposition of a simple pairs trading strategy on two stocks (Bajaj Finserv and Indian Bank) in the Nifty500, at the one-minute time frequency, in order to demonstrate some of the core ideas of statistical arbitrage strategies.

Forecast the Airlines Passengers and CocaCola Prices data set. Prepare a document for model explaining. How many dummy variables you have created and RMSE value for model. Finally which model you will use for Forecasting.

These functions automate the process of checking for unit roots and transforming data to stationarity

Statistical tests of time series using python