Time Series

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


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).

SYLLABUS (lecture by lecture):


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

Lab: Questions 1 and 2 of Lab 1.


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

Lab: Lab 2.


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

Lab: Lab 3.


Class: Holt Winters methods: additive and multiplicative.

Lab: Lab 4.


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

Lab: Lab 5.


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").


Class: Kalman filter models; Updating scheme for KF.

Lab: Lab 7.


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

Lab: Lab 8.