- 9-10am Monday Lecture in LB01 Lloyd Building
- 4-5pm Monday Lecture in LB01 Lloyd Building
- 10-11am Friday Lecture LB04 Lloyd Building
5 ECTS module corresponds to about 100-125 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. The new academic year structure is dense and good time management is essential.
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.
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.
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.
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.
- W1: Introduction, visualisation, SES and DES algorithms.
- W2: SHW+, SHWx
W3: ACF, PACF, Linear model, AR(1) (e.g. reference chapter 9 lecturenotes)
- Assignment P1 published on Blackboard, 24/09/2018
- W4: PACF, AR(p), Least Squares solutions and prediction intervals (chapter 10 lecturenotes)
- W5: MA(q) and ARIMA(p,d,q)
- W6: Seasonal ARIMA, BIC and AIC (cf. chap.10-17 in Lecturenotes). See also R code simulation of seasonal Arima time series. Using ACF and PACF to find best ARIMA model: case study 'mink' time series (fma package).
- W7: Reading week - Assignment P2 published on Blackboard, 24/10/2018
- W8: Monday bank holiday- Friday: fitting ARIMA to time series 'dole' and 'elec'
- W9: Transformation of time series (chapter 18). Lab notes transformation of time series. Friday 9/11/2018: Feedback First Assignment.
W10: Approximations of AIC and BIC - Exercises
- Assignment P3 on ARIMA published on Blackboard, 12/11/2018
- W11: Conclusions - R programming - No lecture Friday 23/11/2018
- W12: Feedback P2 - Question time assignments P3.
Exam/AssessmentFirst session 100% Assessment (no exam in Trinity term 2018):
- see Blackboard
- Repeat Exam 100% (Michaelmas term 2019)