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...

Full description

Bibliographic Details

Published in:	Physica A Vol. 678; p. 130869
Main Authors:	Munyao, Jackson Ndoto, Oluoch, Lillian Achola, Iftikhar, Hasnain, Rodrigues, Paulo Canas
Format:	Journal Article
Language:	English
Published:	Elsevier B.V 15.11.2025
Subjects:	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 Access:	Get full text
Tags:	Add Tag No Tags, Be the first to tag this record!