Recurrent neural networks for hierarchical time series forecasting: An application to the S&P 500 market value

This paper investigates the use of Recurrent Neural Networks (RNNs), specifically Long Short-Term Memory (LSTM) and Gated Recurrent Unit (GRU) architectures, for hierarchical time series forecasting in financial markets. Using market value data from the top 70 companies in the S&P 500 index, we...

Ausführliche Beschreibung

Bibliographische Detailangaben

Veröffentlicht in:	Physica A Jg. 678; S. 130869
Hauptverfasser:	Munyao, Jackson Ndoto, Oluoch, Lillian Achola, Iftikhar, Hasnain, Rodrigues, Paulo Canas
Format:	Journal Article
Sprache:	Englisch
Veröffentlicht:	Elsevier B.V 15.11.2025
Schlagworte:	ARIMA Forecasting Hierarchical time series Hierarchical time series forecasting LSTM and GRU Recurrent neural networks (RNNs) S&P 500 S&P 500 ARIMA LSTM and GRU Hierarchical time series forecasting Recurrent neural networks (RNNs) Forecasting Hierarchical time series
ISSN:	0378-4371
Online-Zugang:	Volltext
Tags:	Tag hinzufügen Keine Tags, Fügen Sie den ersten Tag hinzu!