Time series forecasting | Python implements AR, ARMA, ARIMA time series forecasting

Time series forecasting | MATLAB implements VAR and GARCH time series forecasting

predictive effect

basic introduction

Python implements AR, ARMA, ARIMA time series forecasting

Model principle

AR, ARMA, and ARIMA are commonly used time series forecasting methods. The main difference between them is the number and order of autoregressive items and moving average items included in the model.
The AR model (Autoregressive Model) is a model that only contains autoregressive items. Its basic idea is to establish a linear relationship between the value of the current moment and the values ​​​​of several past moments, and use these historical data to predict future values. The order p of the AR model represents the number of autoregressive items contained in the model, and the best model can be obtained by fitting the optimal p value.
The ARMA model (Autoregressive Moving Average Model) is a model that also includes autoregressive