Construction of Confidence Interval for a Univariate Stock Price Signal Predicted Through Long Short Term Memory Network

In this paper, we show an innovative way to construct bootstrap confidence interval of a signal estimated based on a univariate LSTM model. We take three different types of bootstrap methods for dependent set up. We prescribe some useful suggestions to select the optimal block length while performin...

Descripción completa

Detalles Bibliográficos
Autores principales: De, Shankhajyoti, Dey, Arabin Kumar, Gouda, Deepak Kumar
Formato: Online Artículo Texto
Lenguaje:English
Publicado: Springer Berlin Heidelberg 2020
Materias:
Article
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC7373837/
http://dx.doi.org/10.1007/s40745-020-00307-8
_version_ 1783561569504854016
author De, Shankhajyoti
Dey, Arabin Kumar
Gouda, Deepak Kumar
author_facet De, Shankhajyoti
Dey, Arabin Kumar
Gouda, Deepak Kumar
author_sort De, Shankhajyoti
collection PubMed
description In this paper, we show an innovative way to construct bootstrap confidence interval of a signal estimated based on a univariate LSTM model. We take three different types of bootstrap methods for dependent set up. We prescribe some useful suggestions to select the optimal block length while performing the bootstrapping of the sample. We also propose a benchmark to compare the confidence interval measured through different bootstrap strategies. We illustrate the experimental results through some stock price data set.
format Online
Article
Text
id pubmed-7373837
institution National Center for Biotechnology Information
language English
publishDate 2020
publisher Springer Berlin Heidelberg
record_format MEDLINE/PubMed
spelling pubmed-73738372020-07-22 Construction of Confidence Interval for a Univariate Stock Price Signal Predicted Through Long Short Term Memory Network De, Shankhajyoti Dey, Arabin Kumar Gouda, Deepak Kumar Ann. Data. Sci. Article In this paper, we show an innovative way to construct bootstrap confidence interval of a signal estimated based on a univariate LSTM model. We take three different types of bootstrap methods for dependent set up. We prescribe some useful suggestions to select the optimal block length while performing the bootstrapping of the sample. We also propose a benchmark to compare the confidence interval measured through different bootstrap strategies. We illustrate the experimental results through some stock price data set. Springer Berlin Heidelberg 2020-07-22 2022 /pmc/articles/PMC7373837/ http://dx.doi.org/10.1007/s40745-020-00307-8 Text en © Springer-Verlag GmbH Germany, part of Springer Nature 2020 This article is made available via the PMC Open Access Subset for unrestricted research re-use and secondary analysis in any form or by any means with acknowledgement of the original source. These permissions are granted for the duration of the World Health Organization (WHO) declaration of COVID-19 as a global pandemic.
spellingShingle Article
De, Shankhajyoti
Dey, Arabin Kumar
Gouda, Deepak Kumar
Construction of Confidence Interval for a Univariate Stock Price Signal Predicted Through Long Short Term Memory Network
title Construction of Confidence Interval for a Univariate Stock Price Signal Predicted Through Long Short Term Memory Network
title_full Construction of Confidence Interval for a Univariate Stock Price Signal Predicted Through Long Short Term Memory Network
title_fullStr Construction of Confidence Interval for a Univariate Stock Price Signal Predicted Through Long Short Term Memory Network
title_full_unstemmed Construction of Confidence Interval for a Univariate Stock Price Signal Predicted Through Long Short Term Memory Network
title_short Construction of Confidence Interval for a Univariate Stock Price Signal Predicted Through Long Short Term Memory Network
title_sort construction of confidence interval for a univariate stock price signal predicted through long short term memory network
topic Article
url https://www.ncbi.nlm.nih.gov/pmc/articles/PMC7373837/
http://dx.doi.org/10.1007/s40745-020-00307-8
work_keys_str_mv AT deshankhajyoti constructionofconfidenceintervalforaunivariatestockpricesignalpredictedthroughlongshorttermmemorynetwork
AT deyarabinkumar constructionofconfidenceintervalforaunivariatestockpricesignalpredictedthroughlongshorttermmemorynetwork
AT goudadeepakkumar constructionofconfidenceintervalforaunivariatestockpricesignalpredictedthroughlongshorttermmemorynetwork

Ejemplares similares