Supervisor
Sam Weiss
Programme
MSc in Data Analytics
Subject
Computer Science
Abstract
This study compares the performance of two deep learning architectures, the Temporal Fusion Transformer (TFT) and N-BEATS, for 10-day stock price forecasting. Both models were implemented using the Darts Python library, which ensured consistent preprocessing, training, and evaluation. The dataset, sourced from Yahoo Finance, included daily equity prices, technical indicators, a market sentiment index, and earnings announcements.
TFT was applied as a multivariate model incorporating past, future, and static covariates, while N-BEATS was trained as separate univariate models with past covariates only. A rolling forecast cross-validation approach was used for evaluation. Results show that TFT consistently outperformed N-BEATS, particularly under volatile conditions. Moreover, TFT’s interpretability features identified the influence of technical and event-based variables, making it a more effective tool for financial forecasting.
Date of Award
2025
Full Publication Date
2025
Access Rights
open access
Document Type
Capstone Project
Resource Type
thesis
Recommended Citation
Fleury, F.
(2025) Time Series Forecasting in Financial Markets: Benchmarking the Temporal Fusion Transformer Against N-BEATS. CCT College Dublin.
DOI: https://doi.org/10.63227/652.299.117