An essential business use of forecasting is time series forecasting. Practically everything a company or corporation undertakes requires foresight into the requirements of the future, including sales, inputs/intermediates, and people, so that these may be budgeted for or planned for. Both the more recent deep learning approaches and the more traditional statistically based approaches, such ARIMA modelling, have well-developed time series techniques. This book provides an overview of the key time series forecasting approaches, outlines the fundamental presuppositions behind each technique, and discusses possible application scenarios. Understanding the theory and rationale of diverse time series forecasts, the underlying assumptions and limitations, and the pertinent applications for each are the main goals.