PHAROS Training Series - Course 4 "Introduction to Time Series Forecasting"

Monday 9 Mar 2026, 11:30 → 16:15 Europe/Athens

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.

 

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11:30 → 11:40 The Pharos Training Series

Speaker: Nikos Bakas (GRNET)

11:40 → 12:10 Introduction to Timeseries Forecasting

Speaker: Nikos Bakas (GRNET)

12:10 → 12:30 Forecasting & AI: Live Q&As with Professor Spyros Makridakis

Speaker: Prof. Spyros Makridakis (University of Nicosia)

12:30 → 12:40 Introduction & Objectives

Overview of time series forecasting, webinar goals, structure, and expected outcomes.

12:40 → 13:10 Theory Session I: Fundamentals of Time Series Analysis

Definition of time series data, key components, the importance of forecasting, and the role of data analysis.

13:10 → 13:55 Hands-On Session I: Data Analysis & Statistical Models

Practical implementation of data preprocessing, exploratory analysis, and classical statistical forecasting models.

13:55 → 14:25 Theory Session II: Machine Learning & Deep Learning for Time Series

Concepts, architectures, and methodologies for machine learning and deep learning–based forecasting.

14:25 → 15:10 Hands-On Session II: ML & DL Forecasting Models

End-to-end development, training, and evaluation of machine learning and deep learning models.

15:10 → 15:50 Core Use Case: End-to-End Forecasting Application

A complete real-world case study integrating all theoretical and practical elements covered in the webinar.

15:50 → 16:05 Live Q&A Session

Open discussion and clarification of concepts, methods, and implementation details.

16:05 → 16:15 Wrap-Up, Key Takeaways & Resources

Summary of key insights, lessons learned, and resources for further study and application.