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Compulsory

2

On completing the course, the student will be able to:

• Understand linear and nonlinear forecasting models.
• Be able to understand the limits of validity of predictions.
• Explains the results of the forecasts.
• Understand nonlinear dynamics.
• Understand how to apply various prediction algorithms.
• Be able to apply forecasting processes to real data.

• Search for, analysis and synthesis of data and information, with the use of the necessary technology
• Decision Making
• Teamwork
• Production of free, creative, and inductive thinking

This course aims in providing solid knowledge on a domain that is beneficial to those studying AI and machine learning. Timeseries analysis and forecasting is a domain where computer science, and coding meet mathematics, physics and other natural sciences, engineering, economics, finance, and social sciences. Comprehensive knowledge on the theoretical foundations of the area (fundamental principles, elements etc.) is offered. The course includes timeseries analysis by utilizing both linear approaches and nonlinear dynamics. Both modules move towards the final goal which is timeseries forecasting for practical applications.Optimization Techniques.

• Introduction to time series analysis.
• Forecasting utilizing linear time series models (ARMA, ARIMA, SARIMA etc.).
• Basic characteristics of nonlinear timeseries and their analysis.
• Nonlinear time series forecasting methods and models.