PHAROS AI Factory announces the 14th Course of its Training Series, under the title “Machine & Deep Learning for Time Series forecasting“, under thetopic Deep Learning, held online via Zoom on September 15th, 2026.
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
Location: Online via Zoom (you will get the zoom link upon registration)
Learning Objectives:
By the end of the course, participants will be able to:
- 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.
The course’s agenda can be found here.
Register to attend by filling out the form here.
