After searching a lot I realized people prefer using the libraries directly for ARIMA and forecasting. To gain a better understanding,I decided to write the thing from scratch using numpy and pandas. If you feel the same way, continue reading :)

ARIMA is a model used for time-series forecasting . It has 3 main parts : Making the data stationary, AR (Auto Regression ) and MA (Moving Average). We’ll start with differencing the data ,then estimate the data using the AR ,use MA on the errors generated, un-difference the data and check the results.

What the following article contains : Code for the model and an explanation for each step

#numpy #arima #python