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Time Series and Forecasting
Engineering & Social Sciences Program
Madrid, Spain
Dates: mid Jan 2026 - early Jun 2026
Time Series and Forecasting
OVERVIEW
CEA CAPA Partner Institution: Universidad Carlos III de Madrid
Location: Madrid, Spain
Primary Subject Area: Computer Engineering
Instruction in: English
Course Code: 17312
Transcript Source: Partner Institution
Course Details: Level 400
Recommended Semester Credits: 3
Contact Hours: 42
Prerequisites: Introduction to Statistical Modeling
Statistical Signal Processing
Predictive Modeling
DESCRIPTION
1. Introduction to time series
1.1 Examples of univariate time series
1.2 Examples of multivariate time series
1.3 Software for time series analysis
2. Time series decomposition.
2.1 Time series components.
2.2 Classical decomposition.
2.3 ARIMA decomposition.
2.4 STL decomposition.
2.5 Forecasting with decomposition.
2.7 Exponential smoothing techniques.
3. ARIMA models.
3.1 Stationarity and differencing.
3.2 Backshift notation
3.3 Autoregressive models.
3.4 Moving average models.
3.5 Non-seasonal ARIMA models.
3.6 Estimation and order selection.
3.7 Seasonal ARIMA models.
3.7 Forecasting with ARIMA models.
4. Advanced forecasting methods.
4.1 Dynamic regression models.
4.2 Vector autoregressions.
4.3 Dynamic factorial models.
4.4 Forecasting hierarchical or grouped time series.
5. Conditional heteroscedastic models.
5.1 GARCH models.
5.2 Statistical properties.
5.3 Estimating parameters and volatilities
1.1 Examples of univariate time series
1.2 Examples of multivariate time series
1.3 Software for time series analysis
2. Time series decomposition.
2.1 Time series components.
2.2 Classical decomposition.
2.3 ARIMA decomposition.
2.4 STL decomposition.
2.5 Forecasting with decomposition.
2.7 Exponential smoothing techniques.
3. ARIMA models.
3.1 Stationarity and differencing.
3.2 Backshift notation
3.3 Autoregressive models.
3.4 Moving average models.
3.5 Non-seasonal ARIMA models.
3.6 Estimation and order selection.
3.7 Seasonal ARIMA models.
3.7 Forecasting with ARIMA models.
4. Advanced forecasting methods.
4.1 Dynamic regression models.
4.2 Vector autoregressions.
4.3 Dynamic factorial models.
4.4 Forecasting hierarchical or grouped time series.
5. Conditional heteroscedastic models.
5.1 GARCH models.
5.2 Statistical properties.
5.3 Estimating parameters and volatilities