Analyzing crises in global financial indices using Recurrent Neural Network based Autoencoder.

Analyzing crises in global financial indices using Recurrent Neural Network based Autoencoder.

In this study, we present a novel approach to analyzing financial crises of the global stock market by leveraging a modified Autoencoder model based on Recurrent Neural Network (RNN-AE). We analyze time series data from 24 global stock markets between 2007 and 2024, covering multiple financial crise...

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Bibliographic Details
Main Authors: Mimusa Azim Mim, Md Kamrul Hasan Tuhin, Ashadun Nobi
Format: Article
Language:English
Published: Public Library of Science (PLoS) 2025-01-01
Series:PLoS ONE
Online Access:https://doi.org/10.1371/journal.pone.0326947
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