• Online, Self-Paced
Course Description

Time series forecasting uses data collected over periodic intervals to analyze how the variable changes over time. Time series analysis is often used for forecasting problems such as demand forecasting and revenue forecasting.

In this course, discover how time series analysis works and how time series models such as the autoregressive integrated moving average (ARIMA) model can help forecast future values of time-varying data using historical values. Next, visualize and explore time series data using windowing, differencing, moving averages, and time series decomposition. Then fit a function, a seasonal component model, and an ARIMA model on this data for forecasting future values. Finally, use association rule learning for market basket analysis to analyze transaction data from a grocery store and perform association rule learning on this data to figure out what items are frequently bought together.

When you are finished with this course you will have the skills to use RapidMiner for time-series forecasting and market basket analysis.

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