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Research Techniques for Prediction
Engineering & Social Sciences Program
Madrid, Spain
Dates: 1/18/24 - 6/5/24
Research Techniques for Prediction
OVERVIEW
CEA CAPA Partner Institution: Universidad Carlos III de Madrid
Location: Madrid, Spain
Primary Subject Area: Mathematics
Instruction in: English
Course Code: 17658
Transcript Source: Partner Institution
Course Details: Level 300, 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.
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.
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.
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.
5. Conditional heteroscedastic models.
5.1 GARCH models.
5.2 Statistical properties.
5.3 Estimating parameters and volatilities.