Airline passenger prediction using ARIMA | Jupyter Notebook

Hi Everyone, I will share the Jupyter Notebook for one of my projects that I did using ARIMA a few years back. Since the main focus is on the code implementation, I will not go into details of ARIMA. However brief description of concepts is important.

ARIMA is an acronym that stands for AutoRegressive Integrated Moving Average. It is a class of models that captures a suite of different standard temporal structures in time series data.

AR : Autoregression. A model that uses the dependent relationship between an observation and some number of lagged observations.
I : Integrated. The use of differencing of raw observations (e.g. subtracting an observation from an observation at the previous time step) in order to make the time series stationary.
MA : Moving Average. A model that uses the dependency between an observation and a residual error from a moving average model applied to lagged observations

The parameters of the ARIMA model are defined as follows:

p: The number of lag observations included in the model, also called the lag order.
d: The number of times that the raw observations are differenced, also called the degree of differencing.
q: The size of the moving average window, also called the order of moving average

Lets move to the implementation part:

#import the libraries…