Curso International Forecasting

This course provides a comprehensive introduction to quantitative forecasting methods essential for decision-making in the global business environment. The curriculum focuses on the theoretical foundations and practical applications of time series analysis, enabling students to model and predict complex economic and financial variables.

Students will move from understanding the basic properties of time series data to implementing advanced econometric models. The course emphasizes the balance between mathematical rigor and practical relevance, using real-world datasets such as exchange rates, GDP growth, stock market indices, and inflation rates.

By the end of this course, students will be able to:

• Analyze Data Structure: Identify key components of time series data, including trends, seasonality, cyclicality, and noise.

• Check Stationarity: Perform unit root tests (e.g., Augmented Dickey-Fuller) to determine the stability of data over time.

• Build Models: Construct, estimate, and validate univariate and multivariate forecasting models.

• Evaluate Accuracy: Utilize statistical metrics (MAE, RMSE, MAPE) to compare model performance and select the most predictive approach.

• Apply Software: Use statistical software (such as R, Python) to automate forecasting processes.