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.