This book offers an accessible introduction to the most current thinking in and practicality of forecasting techniques in the context of time-oriented data. Analyzing time-oriented data and forecasting are among the most important problems that analysts face across many fields, ranging from finance and economics to production operations and the natural sciences. As a result, there is a widespread need for large groups of people in a variety of fields to understand the basic concepts of time series analysis and forecasting. "Introduction to Time Series Analysis and Forecasting" presents the time series analysis branch of applied statistics as the underlying methodology for developing practical forecasts, and it also bridges the gap between theory and practice by equipping readers with the tools needed to analyze time- oriented data and construct useful, short- to medium-term, statistically based forecasts. Seven easy-to-follow chapters provide intuitive explanations and in-depth coverage of key forecasting topics, including: regression-based methods, heuristic smoothing methods, and general time series models; basic statistical tools used in analyzing time series data; metrics for evaluating forecast errors and methods for evaluating and tracking forecasting performance over time; cross-section and time series regression data, least squares and maximum likelihood model fitting, model adequacy checking, prediction intervals, and weighted and generalized least squares; exponential smoothing techniques for time series with polynomial components and seasonal data; and, forecasting and prediction interval construction with a discussion on transfer function models as well as intervention modeling and analysis. It also discusses: multivariate time series problems, ARCH and GARCH models, and combinations of forecasts; the ARIMA model approach with a discussion on how to identify and fit these models for non-seasonal and seasonal time series; and, the intricate role of comput