ST3010: Applied Forecasting
Michaelmas Term 2016
Lecturer: Rozenn Dahyot
Timetable
 910am Monday Lecture in LB01 Lloyd Building
 45pm Monday Lecture in LB01 Lloyd Building
 1011am Friday Lecture LB04 Lloyd Building
Forewords
ST3010 is a 5 ECTS module which corresponds to about 100+ hours of study time (including the 30+ hours of lectures organised on campus). For your independent personal work in ST3010, you are asked to learn to use the software R for forecasting. Other sources of information are available to help you (e.g. online ressources such as webpages, videos, MOOCs, and books in TCD libraries). Be creative and independent to build up your skills!
R Software
Playing with data is an important part of Statistics modules. Help is given on this webpage for using R in the context of time series. It is advised to do this labs on your own study time to get to be familiar with using R for forecasting.
Lecturenotes
All lecturenotes may not be available electronically. It is advised to come to classes and take notes. A few handouts will be available below. Some incomplete Lecturenotes are available.
Exam/Assessment
 Group Assignment (30%)
 Exam (70%)
 Repeat Exam (100%)
References
There are a lot of books on time series, forecasting in the Library that are relevant to the course. Below the first book is given as an example reference. It is available in the library and most of the time series used in the labs are explained in that book.
 Forecasting  Methods and Applications S. Makridakis, S. C. Wheelwright and R. J. Hyndman, Wiley
 Forecasting: principles and practice online book by R. Hyndman and G. Athanasopoulos
Weekly timeline
 W1: Introduction, SES and DES algorithms.
 W2: Seasonal HoltWinters algorithms
 HoltWinters algorithm in Excel cowtemp and beer
 Reading R Outputs for HoltWinters algorithms
 The HoltWinters Approach to Exponential Smoothing: 50 Years Old and Going Strong, P. Goodwin, Foresight 2010
 Draft draft example report of time series elec in fma package done in class 07/10/2016
 W3: ACF & HoltWinters Exercises. Introduction to ARIMA.
 W4: Least Square algorithm, AR(1) models
 W5: ARIMA(p,d,q)
 W6: (Monday bank Holiday)  Friday: team work (assignment)
 W7: Reading week
 W8: AIC and BIC; ARIMA(p,d,q)

W9: Seasonal ARIMA models
 Voluntary submission Part I assignment to get feedback (deadline 12 noon Friday 25/11)
 Greek letters and LaTeX
 R code: Simulation of Seasonal ARIMA

W10: Seasonal ARIMA models, transformations of time series
 Simulation of ARIMA models Exercises
 Transformation of time series (Friday 2/12/2016)
 R notes: Transformation of time series

W11: Exam revisions
 Feedback on Submitted assignement Part I (Friday 9/12/2016)

W12: Group work & Exam Revisions
 Monday: Exam revision  info peer review.
 Friday: Group Work