Long short-term memory autoencoder based network of financial indices

We present a novel approach for analyzing financial time series data using a Long Short-Term Memory Autoencoder (LSTMAE), a deep learning method. Our primary objective is to uncover intricate relationships among different stock indices, leading to the extraction of stock networks. We examine time se...

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Vydané v:	Humanities & social sciences communications Ročník 12; číslo 1; s. 100 - 15
Hlavní autori:	Tuhin, Kamrul Hasan, Nobi, Ashadun, Rakib, Mahmudul Hasan, Lee, Jae Woo
Médium:	Journal Article
Jazyk:	English
Vydavateľské údaje:	London Palgrave Macmillan UK 29.01.2025 Springer Nature B.V Springer Nature
Predmet:	4014/2801 4014/4006 Causality COVID-19 Deep learning Economic crisis Extraction Financial institutions Health care Health services Humanities and Social Sciences Indexes International finance Machine learning Matrices Methods multidisciplinary National debt Networks Pandemics Property Public debt Rates of return Science Science (multidisciplinary) Securities markets Short term Short term memory Stock exchanges Thresholds Time series United States > US
ISSN:	2662-9992, 2662-9992
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