Chapter 12 - Time Series Forecasting

Abstract

Time series forecasting is one of the most widely used applications of data science. This chapter provides a comprehensive overview of time series analysis and forecasting. It starts by pointing out the distinction between standard supervised predictive models and time series forecasting models. It provides an introduction to the different time series forecasting methods, starting with time series decomposition, data-driven moving averages, exponential smoothing, discusses model-driven forecasts including regression, Autoregressive Integrated Moving Average methods, and machine learning-based methods using windowing techniques. The performance evaluation section provides a summary of key metrics to measure and compare the accuracy of the forecasting models. This chapter covers the implementation of forecasting techniques using RapidMiner and R.