Deep Learning for Time-Series Analysis

In many real-world application, e.g., speech recognition or sleep stage classification, data are captured over the course of time, constituting a Time-Series. Time-Series often contain temporal dependencies that cause two otherwise identical points of time to belong to different classes or predict d...

Ausführliche Beschreibung

Gespeichert in:
Bibliographische Detailangaben
1. Verfasser: Gamboa, John Cristian Borges
Format: Artikel
Sprache:eng
Schlagworte:
Online-Zugang:Volltext bestellen
Tags: Tag hinzufügen
Keine Tags, Fügen Sie den ersten Tag hinzu!
container_end_page
container_issue
container_start_page
container_title
container_volume
creator Gamboa, John Cristian Borges
description In many real-world application, e.g., speech recognition or sleep stage classification, data are captured over the course of time, constituting a Time-Series. Time-Series often contain temporal dependencies that cause two otherwise identical points of time to belong to different classes or predict different behavior. This characteristic generally increases the difficulty of analysing them. Existing techniques often depended on hand-crafted features that were expensive to create and required expert knowledge of the field. With the advent of Deep Learning new models of unsupervised learning of features for Time-series analysis and forecast have been developed. Such new developments are the topic of this paper: a review of the main Deep Learning techniques is presented, and some applications on Time-Series analysis are summaried. The results make it clear that Deep Learning has a lot to contribute to the field.
doi_str_mv 10.48550/arxiv.1701.01887
format Article
fullrecord <record><control><sourceid>arxiv_GOX</sourceid><recordid>TN_cdi_arxiv_primary_1701_01887</recordid><sourceformat>XML</sourceformat><sourcesystem>PC</sourcesystem><sourcerecordid>1701_01887</sourcerecordid><originalsourceid>FETCH-LOGICAL-a677-adf1ce8953e2266cdb0b1614bc96e97fe758dcf28a11fee3fc02d1ebbf7c778e3</originalsourceid><addsrcrecordid>eNotzrsKwjAUgOEsDlJ9ACc7ubXm9JKTjuIdCg52L0l6IoFaJQWxby9Wp3_7-RhbAI8zmed8rfzbvWJADjEHKXHKVjuiZ1iS8p3rbqF9-LByd4qu5B314aZT7dC7fsYmVrU9zf8NWHXYV9tTVF6O5-2mjJRAjFRjwZAs8pSSRAjTaK5BQKZNIahAS5jLxthEKgBLlFrDkwZIa4sGUVIasOVvO0Lrp3d35Yf6C65HcPoBSSg68g</addsrcrecordid><sourcetype>Open Access Repository</sourcetype><iscdi>true</iscdi><recordtype>article</recordtype></control><display><type>article</type><title>Deep Learning for Time-Series Analysis</title><source>arXiv.org</source><creator>Gamboa, John Cristian Borges</creator><creatorcontrib>Gamboa, John Cristian Borges</creatorcontrib><description>In many real-world application, e.g., speech recognition or sleep stage classification, data are captured over the course of time, constituting a Time-Series. Time-Series often contain temporal dependencies that cause two otherwise identical points of time to belong to different classes or predict different behavior. This characteristic generally increases the difficulty of analysing them. Existing techniques often depended on hand-crafted features that were expensive to create and required expert knowledge of the field. With the advent of Deep Learning new models of unsupervised learning of features for Time-series analysis and forecast have been developed. Such new developments are the topic of this paper: a review of the main Deep Learning techniques is presented, and some applications on Time-Series analysis are summaried. The results make it clear that Deep Learning has a lot to contribute to the field.</description><identifier>DOI: 10.48550/arxiv.1701.01887</identifier><language>eng</language><subject>Computer Science - Learning</subject><creationdate>2017-01</creationdate><rights>http://arxiv.org/licenses/nonexclusive-distrib/1.0</rights><oa>free_for_read</oa><woscitedreferencessubscribed>false</woscitedreferencessubscribed></display><links><openurl>$$Topenurl_article</openurl><openurlfulltext>$$Topenurlfull_article</openurlfulltext><thumbnail>$$Tsyndetics_thumb_exl</thumbnail><link.rule.ids>228,230,776,881</link.rule.ids><linktorsrc>$$Uhttps://arxiv.org/abs/1701.01887$$EView_record_in_Cornell_University$$FView_record_in_$$GCornell_University$$Hfree_for_read</linktorsrc><backlink>$$Uhttps://doi.org/10.48550/arXiv.1701.01887$$DView paper in arXiv$$Hfree_for_read</backlink></links><search><creatorcontrib>Gamboa, John Cristian Borges</creatorcontrib><title>Deep Learning for Time-Series Analysis</title><description>In many real-world application, e.g., speech recognition or sleep stage classification, data are captured over the course of time, constituting a Time-Series. Time-Series often contain temporal dependencies that cause two otherwise identical points of time to belong to different classes or predict different behavior. This characteristic generally increases the difficulty of analysing them. Existing techniques often depended on hand-crafted features that were expensive to create and required expert knowledge of the field. With the advent of Deep Learning new models of unsupervised learning of features for Time-series analysis and forecast have been developed. Such new developments are the topic of this paper: a review of the main Deep Learning techniques is presented, and some applications on Time-Series analysis are summaried. The results make it clear that Deep Learning has a lot to contribute to the field.</description><subject>Computer Science - Learning</subject><fulltext>true</fulltext><rsrctype>article</rsrctype><creationdate>2017</creationdate><recordtype>article</recordtype><sourceid>GOX</sourceid><recordid>eNotzrsKwjAUgOEsDlJ9ACc7ubXm9JKTjuIdCg52L0l6IoFaJQWxby9Wp3_7-RhbAI8zmed8rfzbvWJADjEHKXHKVjuiZ1iS8p3rbqF9-LByd4qu5B314aZT7dC7fsYmVrU9zf8NWHXYV9tTVF6O5-2mjJRAjFRjwZAs8pSSRAjTaK5BQKZNIahAS5jLxthEKgBLlFrDkwZIa4sGUVIasOVvO0Lrp3d35Yf6C65HcPoBSSg68g</recordid><startdate>20170107</startdate><enddate>20170107</enddate><creator>Gamboa, John Cristian Borges</creator><scope>AKY</scope><scope>GOX</scope></search><sort><creationdate>20170107</creationdate><title>Deep Learning for Time-Series Analysis</title><author>Gamboa, John Cristian Borges</author></sort><facets><frbrtype>5</frbrtype><frbrgroupid>cdi_FETCH-LOGICAL-a677-adf1ce8953e2266cdb0b1614bc96e97fe758dcf28a11fee3fc02d1ebbf7c778e3</frbrgroupid><rsrctype>articles</rsrctype><prefilter>articles</prefilter><language>eng</language><creationdate>2017</creationdate><topic>Computer Science - Learning</topic><toplevel>online_resources</toplevel><creatorcontrib>Gamboa, John Cristian Borges</creatorcontrib><collection>arXiv Computer Science</collection><collection>arXiv.org</collection></facets><delivery><delcategory>Remote Search Resource</delcategory><fulltext>fulltext_linktorsrc</fulltext></delivery><addata><au>Gamboa, John Cristian Borges</au><format>journal</format><genre>article</genre><ristype>JOUR</ristype><atitle>Deep Learning for Time-Series Analysis</atitle><date>2017-01-07</date><risdate>2017</risdate><abstract>In many real-world application, e.g., speech recognition or sleep stage classification, data are captured over the course of time, constituting a Time-Series. Time-Series often contain temporal dependencies that cause two otherwise identical points of time to belong to different classes or predict different behavior. This characteristic generally increases the difficulty of analysing them. Existing techniques often depended on hand-crafted features that were expensive to create and required expert knowledge of the field. With the advent of Deep Learning new models of unsupervised learning of features for Time-series analysis and forecast have been developed. Such new developments are the topic of this paper: a review of the main Deep Learning techniques is presented, and some applications on Time-Series analysis are summaried. The results make it clear that Deep Learning has a lot to contribute to the field.</abstract><doi>10.48550/arxiv.1701.01887</doi><oa>free_for_read</oa></addata></record>
fulltext fulltext_linktorsrc
identifier DOI: 10.48550/arxiv.1701.01887
ispartof
issn
language eng
recordid cdi_arxiv_primary_1701_01887
source arXiv.org
subjects Computer Science - Learning
title Deep Learning for Time-Series Analysis
url https://sfx.bib-bvb.de/sfx_tum?ctx_ver=Z39.88-2004&ctx_enc=info:ofi/enc:UTF-8&ctx_tim=2025-01-25T07%3A41%3A04IST&url_ver=Z39.88-2004&url_ctx_fmt=infofi/fmt:kev:mtx:ctx&rfr_id=info:sid/primo.exlibrisgroup.com:primo3-Article-arxiv_GOX&rft_val_fmt=info:ofi/fmt:kev:mtx:journal&rft.genre=article&rft.atitle=Deep%20Learning%20for%20Time-Series%20Analysis&rft.au=Gamboa,%20John%20Cristian%20Borges&rft.date=2017-01-07&rft_id=info:doi/10.48550/arxiv.1701.01887&rft_dat=%3Carxiv_GOX%3E1701_01887%3C/arxiv_GOX%3E%3Curl%3E%3C/url%3E&disable_directlink=true&sfx.directlink=off&sfx.report_link=0&rft_id=info:oai/&rft_id=info:pmid/&rfr_iscdi=true