PHAROS AI Factory announces the 14th Course of its Training Series, under the title "Machine & Deep Learning for Time Series forecasting", under the topic Deep Learning, held online via Zoom.
Date: September 15th, 2026, at 11:00 EEST
Location: Online via Zoom
Presentation Language: Greek
Course Description: This hands-on session works through a complete forecasting pipeline in Python related to time-series data. Participants begin with preprocessing (handling missing values, outliers, stationarity, and feature engineering), then move through classical machine learning models, gradient-boosted trees, and deep learning architectures including LSTMs, temporal convolutional networks, and Transformer-based models such as PatchTST. The course is built around realistic use cases, working with live coding alongside participants.
Audience: ML Engineers, Data Scientists, Academic Researchers, Business Owners
Prerequisites: Python
Learning Objectives:
- Prepare time series data for supervised learning (handling irregular sampling, missing values and outliers — while avoiding the data leakage patterns that invalidate most forecasting results).
- Select appropriate baselines and evaluation metrics, and apply walk-forward validation rather than standard cross-validation.
- Build and compare classical, gradient-boosting and deep learning forecasting models, and judge when the added complexity of deep learning is justified.
- Recognise the strengths and limitations of current architectures, from LSTMs and TCNs to Transformer-based and foundation models.
Instructor's profile:
- Efterpi Paraskevoulakou, UPRC
Efterpi Paraskevoulakou is a researcher and practitioner working at the intersection of applied machine learning and large-scale systems. She holds a PhD from the University of Piraeus (Department of Digital Systems) on AI-driven resource and service management across the cloud-edge continuum, with an M.Sc. in Big Data Analytics and a B.Sc. in Economics from the same institution. She has published in leading peer-reviewed journals and international conferences, including IEEE Transactions on Network and Service Management and Future Generation Computer Systems, and received a Best Paper Award at CCWC 2024. She has also participated in several EU-funded research projects focused on AI-driven orchestration in cloud-edge systems.
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