PHAROS AI Factory announces the 4th Course of its Training Series, under the title "Introduction to Time Series Forecasting", Track "Machine Learning", held online via Zoom.
Date: March 9th, 2026, at 11:30 EET
Location: Online via Zoom
Presentation Language: Greek
Audience: This course is intended for postgraduate students in computer science and economics, researchers, employees of small and medium-sized enterprises (SMEs), data scientists, and machine learning engineers, as well as anyone interested in applying time series forecasting techniques to real-world, data-driven problems.
Learning Objectives:
This course provides a complete, hands-on introduction to time series forecasting, starting from the fundamentals of what time series data is and why its analysis is essential. Participants will learn why forecasting plays a critical role in decision making, how data analysis and preprocessing are integrated into the forecasting pipeline, and how insights are extracted from temporal data. The session covers both classical statistical models and modern machine learning and deep learning approaches for time series forecasting. Through practical, end-to-end examples, attendees will implement forecasting models, understand the theory behind them, and apply these techniques to real-world scenarios.
Learning Outcomes: Solve a specific problem
After completing this course, participants will be able to explain the key principles of time series analysis and forecasting covered in the session, analyze time series data to identify patterns and trends, and build forecasting solutions using statistical, machine learning, and deep learning models. Attendees will also be able to evaluate forecasting results, apply the learned techniques to real-world problems, and identify clear next steps for further learning or practical implementation.
Instructor's profile:
The course will be led by a PhD researcher with expertise in time series analysis, machine learning, and data-driven modeling, combining academic depth with practical experience.
Prerequisites: Participants should have intermediate-level knowledge of the Python programming language and a basic understanding of data analysis concepts.
Note: Please enter your institutional/corporate email when registering.
