ARIMA stands for AutoRegressive Integrated Moving Average. It is a popular method for time series forecasting that uses the past values and errors to predict future values.

AutoRegressive (AR): This component represents the relationship between a current observation and a certain number of its previous observations.

Integrated (I): The integrated component represents the differencing needed to make the time series stationary.

Moving Average (MA): The moving average component represents the relationship between the current observation and a certain number of past error terms.


★ TABLE OF CONTENTS ★
0:00 intro: Why Arima
1:00 Arima: What are the components of ARIMA?
1:25 Arima components: Auto regressive explanation with example
2:25 Arima components: Integrated explanation with example
3:20 Arima components: Moving average explanation with example
4:23 Code: Basic example of Arima in Python
8:47 ARIMA_PLUS: what makes it different from ARIMA
10:09 Bigquery: Arima_plus example in big query
11:13 Bigquery: visualizing time series raw data
11:54 Bigquery: creating the arima_plus model
15:35 Bigquery: evaluating arima_plus model
16:51 Bigquery: forecasting time series with arima_plus model
17:49 Looker studio: plotting forecasted time series values
19:30 Outro