No-code ML with RapidMiner: Time-series Forecasting & Market Basket Analysis

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.