Timeseries & Forecasting

About this course

Course Overview:Time series data is prevalent in various fields, from finance and economics to climate science and industrial applications. This course provides a comprehensive introduction to time series analysis and forecasting techniques, enabling participants to understand and model time-dependent data patterns. Through a combination of theoretical concepts, practical exercises, and real-world projects, participants will develop the skills necessary to analyze historical data, identify trends, seasonality, and make accurate predictions for future observations.Assessment and Certification:To ensure a comprehensive understanding of the course material, participants will be assessed through various means. Assignments will be given regularly to reinforce theoretical knowledge and apply it to practical scenarios. These assignments may involve data analysis, forecasting, and model evaluation tasks.Additionally, participants will undertake a final project that spans multiple weeks, where they will work on a real-world time series forecasting problem of their choice. They will be required to collect and preprocess data, apply appropriate forecasting techniques, and present their findings in a written report and a final presentation. The project will assess their ability to apply the acquired knowledge independently.Target Audiences:The course is tailored to suit the needs of the following target audience:Data Science and Analytics Professionals: Data scientists, analysts, and researchers who want to add time series analysis and forecasting techniques to their skillset for understanding patterns and making predictions in time-dependent data.Business and Economics Professionals: Individuals working in business and economics fields who need to analyze historical data to make informed decisions, conduct demand forecasting, inventory management, and business cycle analysis.Finance Professionals: Finance expert

Course objectives

• understand time series analysis fundamentals
• apply theoretical knowledge to practical tasks
• evaluate forecasting models effectively