Checking date: 30/06/2021

Course: 2021/2022

Time Series Analysis
Study: Master in Statistics for Data Science (345)


Department assigned to the subject: Department of Statistics

Type: Electives
ECTS Credits: 3.0 ECTS


Knowledge acquisition of: 1) univariate time series models; 2) multivariate time series models; 3) stochastic volatility models; 4) network analysis and connectivity; 5) visualization techniques in networks; 6) graphical models and modelling of dependency; 7) hidden Markov models; 8) estimation and interpretation of hidden Markov models; 9) basis representation of functional data; 10) regression models with functional prediction/response; 11) classification with functional data.
Skills and learning outcomes
Description of contents: programme
1. Basic concepts in Time Series Analysis. 1.1. Random samples and properties of time series. 1.2. Decomposition of a time series: trend, seasonality, cycle and noise. 1.3. Stationary transformations for trend and seasonal. 1.4. Deterministic and stochastic components. 2. Linear Univariate ARIMA models. 2.1. Sationarity and differencing. 2.2. Autocorrelation function and its estimation. 2.3. Autoregressive models AR(p). 2.4. Moving Average models MA (q). 2.5. Non seasonal ARIMA models. 2.6. Estimation and order of selection. 3.7. Forecasting. 3.8. Seasonal ARIMA models. 3. Volatility models. 3.1. ARCH and GARCH modelling. 3.2. Testing strategy for heterocedastic models. 3.3. Volatility forecast. 4. Multivariate time series 4.1. Time series regression. 4.2. VAR models. 4.3. Cointegration. 4.4. Forecasting properties.
Assessment System
  • % end-of-term-examination 40
  • % of continuous assessment (assigments, laboratory, practicals...) 60
Calendar of Continuous assessment
Basic Bibliography
  • Brockwell P.J. and Davis R.A.. Introduction to Time Series and Forecasting.. Springer.. 2002
  • Enders W.. Applied Econometric Time Series.. Wiley. 2015
  • Hamilton J.. Time Series Analysis.. Princeton University Press. 1994
  • Mills T.C. . The Econometric Modelling of financial Time Series.. Cambridge University Press. 1999

The course syllabus may change due academic events or other reasons.