Student: “Vasso Strouthopoulou”
Program: “Data Science and Information Technologies”
Title: “Transformer-Based Architectures for Financial Time Series Forecasting: From Single-Stock Learning to Cross-Asset Generalization”
Abstract:
This thesis investigates the application of Transformer-based neural architectures to short-term stock price prediction. The study examines three model configurations — a Seq2One Transformer encoder, an Attention-Only model, and a Seq2Seq encoder–decoder architecture — evaluated on daily open prices from NASDAQ-listed stocks across varying sequence lengths and training regimes.
Results demonstrate a clear performance progression across models. The Seq2One architecture establishes a stable baseline but exhibits cumulative error in autoregressive forecasts. The Attention-Only model achieves competitive short-term accuracy by capturing localized temporal dependencies. The Seq2Seq Transformer outperforms both predecessors, generating consistent five-day forecasts with the lowest mean absolute and mean squared errors. A multi-stock experiment further demonstrates that Transformers can generalize learned patterns across unseen assets. Comparative evaluation against an LSTM baseline reveals that the LSTM consistently matches or outperforms the Transformer while training 3–4× faster, highlighting practical trade-offs between recurrent and attention-based architectures.
The findings confirm the flexibility of attention-based models for financial forecasting and underscore the importance of sequence length selection, with 10–30 day input windows offering the best trade-off between responsiveness and noise reduction. Future work may incorporate multi-feature inputs, adaptive fine-tuning, and volatility-aware embeddings to enhance model robustness and interpretability.

Date/Time: July 2, 2026 – 12:00 PM.
Examination Committee:
”
Assoc. Prof. Aggelos Pikrakis
Dr. Kosmas Kritsis
Dr. Konstantinos Koutroumbas
“
Presentation link:
https://unipi.webex.com/meet/webex-host2