Course on Time Series Analysis

JRC-IET European Commission

Petten (The Netherlands) June-July 2012

Lecturers:

• Dr. Andrés M. Alonso (Carlos III University; Madrid, UC3M)

He was born in La Habana in 1968. He obtained a B.S. in Mathematics at the Universidad de La Habana (1991), a M.S. in Epidemiology at the Instituto Pedro Kourí (1994) and a Ph.D in Economics at the Universidad Carlos III de Madrid (2001). He has been lecturer at the Department of Mathematics of the Universidad Autónoma de Madrid and Juan de La Cierva researcher at the Department of Statistics, Universidad Carlos III de Madrid. Now he is Associate Professor of Statistics. His main research interests are: Time series analysis; Resampling techniques; Applied Statistics and Econometrics.

Most Recent publications:

“Trends in ozone concentrations in the Iberian Peninsula by quantile regression and clustering” (with A. Monteiro, A. Carvalho, I. Ribeiro, M. Scotto, S. Barbosa, J.M. Baldasano, M.T. Pay, A.I. Miranda, C. Borrego), Atmospheric Environment, 56, 184-193, 2012.

“Extreme value and cluster analysis of European daily temperature series” (with S. Barbosa and M. Scotto), Journal of Applied Statistics, 38 (12), 2793-2804, 2011.

“Seasonal dynamic factor analysis and bootstrap inference: Application to electricity market forecasting” (with C. García-Martos, J. Rodríguez, J. and M.J. Sánchez), Technometrics, 53 (2), 137–151, 2011.

“Non linear time series clustering based on nonparametric forecast densities” (with J.A. Vilar and J.M. Vilar), Computational Statistics and Data Analysis, 54 (11), 2850-2865, 2010.

“Model based clustering of Baltic sea-level” (with M.Scotto and S. Barbosa), Applied Ocean Research, 31 (1), 4-11, 2009.

• Dr. Carolina García-Martos (Technical University of Madrid,

UPM)

She was born in Madrid in 1981. She obtained a M.Sc. in Industrial Engineering (2005) and a PhD in Engineering with European Mention (2010), both at the Technical University of Madrid (UPM). She has been Research Assistant at the Department of Chemical Engineering, UPM (2004); Trainee at the JRC-European Commission in Petten (two months in 2004); Teaching Assistant and Assistant Professor at the Deparment of Industrial Management, Business Administration and Statistics at UPM (from October 2006 till August 2011); Visiting Researcher at the Vrije Universiteit in Amsterdam (five months during 2009 and 2011). Since September 2011 she is Associate Professor of Statistics at UPM. Her main research interests are: Modelling and forecasting electricity prices, loads and wind power production in liberalized markets, Dimensionality reduction techniques in multivariate time series, Bootstrap methods for state-space models and Simulated Maximum Likelihood via Importance Sampling for Stochastic Volatility Models estimation.

Most Recent publications:

Alonso, A.M., García-Martos, C., Rodríguez, J. and Sánchez, M.J. (2011). "Seasonal Dynamic Factor Analysis and Bootstrap Inference: Application to Electricity Market Forecasting," Technometrics, 53 (2), 137-151.

García-Martos, C., Rodríguez, J. and Sánchez, M.J. (2011). "Forecasting electricity prices and their volatilities using Unobserved Components", Energy Economics, 33 (6), 1227-1239.

García-Martos, C., Rodríguez, J. and Sánchez, M.J. (2012). "Forecasting electricity prices by extracting dynamic common factors: application to the Iberian Market," IET Generation, Transmission & Distribution, 6 (1), 11-20.

García-Martos, C., Rodríguez, J. and Sánchez, M.J. (2012, in press). "Modelling and forecasting fossil fuels, CO2 and electricity prices and their volatilities," Applied Energy.

García-Martos, C. and Conejo, A.J. (forthcoming). “Price Forecasting Techniques in Power Systems,” Wiley Encyclopedia of Electronics and Electrical Engineering.

Objective of the course: The course in Time Series Analysis will illustrate how to build time series models for univariate time series data, and comprises theory and applications. At the end several issues on advanced methods for time series will be introduced (multivariate series, transfer function or conditional heteroskedasticity, among others). Syllabus (20 hours):

• Introduction to time series

• Descriptive analysis of a time series

• Time series and stochastic processes

• Autoregressive, MA and ARMA processes

• Integrated processes

• Seasonal ARIMA processes

• Forecasting with ARIMA models

• Identifying possible ARIMA models

• Estimation and selection of ARIMA models

• Diagnostic checking and prediction