## Time Series Analytics and ForecastingJohn 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:
- 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)
- 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).
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. |