ST3010: Applied Forecasting
Michaelmas Term 2017
Lecturer: Rozenn Dahyot
TA: Matej Ulicny
Timetable
 910am Monday Lecture in LB01 Lloyd Building
 45pm Monday Lecture in LB01 Lloyd Building
 1011am Friday Lecture LB04 Lloyd Building
Foreword
5 ECTS module corresponds to about 100125 hours of study time. This includes the several hours of lectures (30+ for undersgrads, 20+ for postgrads) organised on campus that students have to attend. 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!
Contacting the lecturer
A Discussion Forum is/will be opened online with Blackboard to post queries and is used as the primary tool for discussion on topics related to the course. Emails from students in my taught modules might be overlooked for logistic and time management reasons. If so, please ask questions during classes or flag this email to me directly at the end of the class.
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. Some incomplete Lecturenotes are available. Some additional slides and exercises are available Blackboard.
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 (see chapters 5 to 8 Lecturenotes and Blackboard)
 W3: ACF/PACF  Linear model, AR(1)
 W4: Least Square algorithm, AR(p) models
 W5: ARIMA(p,d,q)

W6: (Monday bank Holiday)  Friday: mock inclass test.
 Some answers of the mock test computed in R
 W7: Reading week

W8: ARIMA(p,d,q)
 9am Monday: Inclass test 25% : bring non programmable calculator.
 4pm Monday: Backshift operator
 Friday: Ex.1 Trinity Exam 2017

W9: Seasonal ARIMA models
 Friday 24/11/2017: Presentation Instruction for Assignment + Feedback answers inclass test(s)
 W10: Seasonal ARIMA models  Transformation of time series

W11: Conclusion and exercises.
 Monday 4pm + Friday 10am: Question time (lecturer available in lecture room for questions from students)

W12:
 9am Monday LB01: Inclass test 25% : bring non programmable calculator.
 Monday 4pm Question time in Westland square lab WS3.1 for assignment (R code)
 Friday 10am: Answers Second In class test  Question time in LB04
Exam/Assessment
First session 100% Assessment (no exam in Trinity term 2018): 2 inclass tests (25% each) in weeks 8 and 12 (50 minutes, on Mondays 9am lectures). After publications of results, email lecturer if more feedback needed.
 Individual time series analysis: report + R code + time series to submit on blackboard (Monday 8th January 2018)
 Repeat Exam 100% (Michaelmas term 2018)