Time series forecasting model for non-stationary series pattern extraction using deep learning and G...
Time series forecasting model for non-stationary series pattern extraction using deep learning and GARCH modeling
About this item
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Publisher
Berlin/Heidelberg: Springer Berlin Heidelberg
Journal title
Language
English
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Publication information
Publisher
Berlin/Heidelberg: Springer Berlin Heidelberg
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Scope and Contents
Contents
This paper presents a novel approach to time series forecasting, an area of significant importance across diverse fields such as finance, meteorology, and industrial production. Time series data, characterized by its complexity involving trends, cyclicality, and random fluctuations, necessitates sophisticated methods for accurate forecasting. Tradi...
Alternative Titles
Full title
Time series forecasting model for non-stationary series pattern extraction using deep learning and GARCH modeling
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Record Identifier
TN_cdi_doaj_primary_oai_doaj_org_article_20791ebe8afa4541ae09ea005bdc5e91
Permalink
https://devfeature-collection.sl.nsw.gov.au/record/TN_cdi_doaj_primary_oai_doaj_org_article_20791ebe8afa4541ae09ea005bdc5e91
Other Identifiers
ISSN
2192-113X
E-ISSN
2192-113X
DOI
10.1186/s13677-023-00576-7
