Time Series Analytics and Forecasting


John von Neumann Institute, Vietnam National University, Ho Chi Minh City

Teaching Assistant: Paul Bui Quang


Monday 29/05/2017 - Friday 09/06/2017

Total hours: 36


Timetable: 


First week:

- Monday 29/05, h. 8.30-11.30 (class), h. 13.00-16.00 (lab)

- Tuesday 30/05, h. 8.30-11.30 (class+lab)

- Wednesday 31/05, h. 8.30-11.30 (class+lab)

- Thursday 01/06, h. 8.30-11.30 (class+lab)

- Friday 02/06, h. 8.30-11.30 (class+lab)


Second week:

- Monday 05/06, h. 8.30-11.30 (class), h. 13.00-16.00 (lab)

- Tuesday 06/06, h. 8.30-11.30 (class+lab)

- Wednesday 07/06, h. 8.30-11.30 (class+lab)

- Thursday 08/06, h. 8.30-11.30 (to be decided)

- Friday 09/06, 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:

Handouts (files are password protected):

29/05/17: Handout 1 (pdf).

30/05/17: Instructions for the individual project (pdf). 

Check out the updated pdf with more detailed instructions (updated parts are highlighted in blue).

30/05/17: Handout 2 (pdf).

31/05/17: Handout 3 (pdf).

01/06/17: Handout 4 (pdf).

02/06/17: Handout 5 (pdf).

05/06/17: Handout 6 (pdf).

06/06/17: Handout 7 (pdf) and examples (R).

07/06/17: Handout 8 (pdf).


Labs:

29/05/17: Lab 1 (pdf,xls) (the pdf was updated).

30/05/17: Lab 2 (pdf,R), data (csv) and answers (R).

31/05/17: Lab 3 (pdf,xls) and answers (xls).

01/06/17: Lab 4 (pdf,xls) and answers (xls).

02/06/17: Lab 5 (pdf,xls).

05/06/17: Lab 6 (pdf,data). 

06/06/17: Lab 7 (pdf,R) and answers (R).

07/06/17: Lab 8 (pdf,R) and data (csv).



SYLLABUS (lecture by lecture):


29/05/17:

Class: Time series plots; Regression; Autoregressive models of order 1 AR(1). 

Lab: Questions 1 and 2 of Lab 1.


30/05/17: 

Class: Autoregressive models of higher order; Transformation of data; Time series decomposition.

Lab: Lab 2.


31/05/17:

Class: Exponential smoothing; Holt linear method; Comparing forecasting methods: RMSE and MAPE.

Lab: Lab 3.


01/06/17:

Class: Holt Winters methods: additive and multiplicative.

Lab: Lab 4.


02/06/17:

Class: Stationarity in mean; Autocorrelation and Partial Autocorrelation Function; Stationarity in Variance; Differencing.

Lab: Lab 5.


05/06/17

Class: Backshift operator; Moving average model (AM); Autoregressive moving average model (ARMA); Autoregressive integrated moving average model (ARIMA); ARIMA model for seasonal data; Qualitative criteria to choose an ARMA model; AIC method for model comparison.

Lab: Lab 6 (Chapter 2 of "Little book of R for time series").


06/06/17

Class: Kalman filter models; Updating scheme for KF.

Lab: Lab 7.


07/06/17

Class: Garch models; ARMA model with GARCH model for the errors.

Lab: Lab 8.