Learning Objectives
Upon completion of the course the students will be able to:  Understand forecasting principles;
 Know how to apply statistical and artificial intelligent techniques to point and probabilistic forecasting;
 Know how to leverage highperformance computing techniques to improve forecasting system efficiency;
 Know how to document and present forecasting methodologies and results.
Text Book  Rob J. Hyndman and George Athanasopoulos (2013) Forecasting: Principles and Practice, OTexts (available online:https://www.otexts.org/fpp)
Reference Books  J. S. Armstrong (2001), Principles of Forecasting: A Handbook for Researchers and Practitioners, Springer
 William W. S. Wei (2005) Time Series Analysis: Univariate and Multivariate Methods, Addison Wesley
 Trevor J. Hastie, Robert J. Tibshirani, & Jerome H. Friedman (2009). The elements of statistical learning: data mining, inference, and prediction. Springer.
Course Contents  Topics/Activities  1  Greetings; intro; terminology; reproducible research  2  Exploratory data analysis  3  Naive models; error analysis  4  Time series analysis (exponential smoothing; ARIMA)  Exam 1  Inclass competition: forecasting with univariate models  5  Regression analysis (multiple regression; logistic regression)  6  L1 / support vector / Fuzzy / semiparametric regression  7  Artificial neural networks  8  Clustering methods  9  Forecast combination methods  Exam 2  Inclass competition: forecasting with explanatory variables  10  Heuristic search; highperformance computing  11  Hierarchical forecasting  12  Probabilistic forecasting (conventional methods; Bayesian methods)  13  Judgmental forecasting; emerging topics  Exam 3  Inclass competition: hierarchical probabilistic forecasting  Project  Phase 1. Literature review Phase 2. Reproducing results from a recent journal paper Phase 3. Forecasting NBA playoff results (or selfdefined forecasting project approved by the instructor) 
Grading  Homework: 6' x 5 = 30'
 Exam: 10' x 3 = 30'
 Project: 10' x 3 = 30'
 Moodle discussion: 10'
