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: 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). 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. |