Total hours: 36 (including labs)

(tentative) Timetable: 

First week:

- Monday 19/03, h. 13.00-16.00 (class)

- Tuesday 20/03, h. 8.30-11.30 (class+lab)

- Wednesday 21/03, h. 13.00-16.00 (class+lab)

- Thursday 22/03, h. 8.30-11.30 (class+lab)

- Friday 23/03, h. 8.30-11.30 (class+lab), h. 13.00-16.00 (lab+lab)

Second week: (hours have changed)

- Monday 26/03, h. 13.00-16.00 (class+lab)

- Tuesday 27/03, h. 13.00-16.00 (class+lab)

- Wednesday 28/03, h. 8.30-11.30 (class), h. 13.00-16.00 (lab)

- Thursday 29/03, h. 8.30-11.30 (tbd)

- Friday 30/03, h 8.30-11.30 (student presentations)

Useful links:

- Central Statistics Office of Ireland (good source of time series data)

- A little book of R for time series, by Avril Choglan

- Meinhold and Singpurwalla (1983), "Understanding the Kalman filter" (pdf).

MATERIAL (it will be uploaded day-by-day):

Handouts (files are password protected):

19/03/18: handouts 1 (pdf), R example (R)

20/03/18: handouts 2 (pdf)

21/03/18: handouts 3 (pdf)

22/03/18: handouts 4 (pdf)

23/03/18: handouts 5 (pdf)

26/03/18: handouts 6 (pdf)

27/03/18: handouts 7 (pdf)

28/03/18: handouts 8 (pdf)

Labs (files are password protected):

20/03/18: lab 1 (pdf,xlsx) and solutions (xlsx)

21/03/18: lab 2 (pdf,R), data (csv) and solutions (R)

22/03/18: lab 3 (pdf,xlsx) and solutions (xlsx)

23/03/18: lab 4 (pdf,xlsx) and solutions (xlsx); lab 5 (pdf,data)

26/03/18: lab 6: continue with lab 5

27/03/18: lab 7 (pdf,R) and solutions (R)

28/03/18: lab 8 (pdf,R,csv) and solutions (R)

Other material 

19/03/18: individual project instructions (pdf). Deadline for the submission is postponed to Monday 16/04/2018.

21/03/18: monthly beer production data (txt). 

Notice that in class we worked with a subsample of this dataset, starting Jan 1991.

SYLLABUS (updated after each class):

19/03/18: introduction to quantitative forecasting; explanatory vs time series models; time series patters; simple linear regression to make forecasting.

20/03/18: autoregressive model AR(m); details of AR(1); transformation of the data to stabilise the variance; other transformations such as month length or trading day adjustment.

21/03/18: time series decomposition; additive (and multiplicative) model; regression trend and seasonal model (with AR component); regression trend and seasonal model (with indicator variables).

22/03/18: exponential smoothing; Holt's linear method; comparing different forecasting methods: RMSE, MAPE.

23/03/18: Holt Winters methods: additive and multiplicative; Stationarity in mean, autocorrelation function; partial autocorrelation function; differencing and seasonal differencing.

26/03/18: Backshift operator; moving average MA models; autoregressive moving average ARMA models; ACF and PACF in ARMA models; autoregressive integrated moving average ARIMA models; Aikake Inforation Criterion AIC. 

27/03/18: ARIMA models with seasonality; Kalman-Filter KF model; derivation of KF updating equations.

28/03/18: GARCH models; GARCH and ARMA; one and k step ahead forecasting with GARCH models.