• Online, Self-Paced
Course Description

Time series forecasting uses data collected over periodic intervals to understand and analyze how the variable changes over time. Time series analysis is used for forecasting problems, such as demand forecasting and revenue forecasting. The auto-regressive integrated moving average (ARIMA) model is widely used for time series forecasting.

In this course, you will see how time series analysis works and how models such as the ARIMA model can help you forecast future values of time-varying data using historical values. You will also learn the differences between stationary and non-stationary time series data.

Next, you will load and explore your time series data for store revenue prediction into BigQuery and visualize and explore this data using Looker Studio.

Finally, you will use an ARIMA model to make revenue forecasts. You will see how BigQuery ML trains multiple ARIMA models to find the best auto-regressive, differencing, and moving average parameters for your data. You will also perform multiple time-series analysis by forecasting store revenue by region.

Learning Objectives

{"discover the key concepts covered in this course"}

Framework Connections

The materials within this course focus on the NICE Framework Task, Knowledge, and Skill statements identified within the indicated NICE Framework component(s):

Specialty Areas

  • Systems Architecture