Univariate forecasting with ARIMA

ARIMA is a univariate time series forecasting method based on two components: an autoregression part and a moving average part. In autoregression, a lag refers to a previous point or points in the time series data that are used to predict future values. For instance, if we’re using a lag of one, we’d use the value observed in the previous time step to model a given observation. The moving average part uses past errors to model the future observations of the time series.

Getting ready

To work with the ARIMA model, you’ll need to install the statsmodels Python package if it’s not already installed. You can install it using pip:

pip install -U statsmodels

For this recipe, we’ll use the same dataset as in the previous recipe.

How to do it…

In Python, you can use the ARIMA model from the statsmodels library. Here’s a basic example of how to fit an ARIMA model:

import pandas as pd
from...