Predictive Analytics and Data Mining

Chapter 10

Time Series Forecasting

Abstract

This chapter provides a high-level overview of time series forecasting and related analysis. It starts by pointing out the clear distinction between standard supervised predictive models and time series forecasting models. It provides a basic introduction to the different time series methods, ranging from data-driven moving averages to exponential smoothing, and also discusses model-driven forecasts including polynomial regression and lag-series-based ARIMA methods. Finally it explains how to implement lag-series-based forecasts using the Windowing operation using RapidMiner. It points out that the implementation of time series in RapidMiner is based on a hybrid concept of transforming series data into ...