Dynamic fine-tuning stacked auto-encoder neural network for weather forecast

Szu-Yin Lin; Chi Chun Chiang; Jung Bin Li; Zih Siang Hung; Kuo-Ming Chao

doi:10.1016/j.future.2018.06.052

Dynamic fine-tuning stacked auto-encoder neural network for weather forecast

Szu-Yin Lin, Chi Chun Chiang, Jung Bin Li, Zih Siang Hung, Kuo-Ming Chao

Research output: Contribution to journal › Article › peer-review

25 Citations (Scopus)

374 Downloads (Pure)

Abstract

With the advent of the big data era, dynamic and real-time data have increased in both volume and variety. It is difficult to make accurate predictions regarding data as they undergo rapid and dynamic changes. Autonomous cloud computing aims to reduce the time required for traditional machine learning. The stacked auto-encoder is a neural network approach in machine learning for feature extraction. It attempts to model high-level abstractions and to reduce data dimensions by using multiple processing layers. However, some common issues may occur during the implementation of deep learning or neural network models, such as over-complicated dimensions of the input data and difficulty in processing dynamic data. Therefore, combining the concept of dynamic data-driven system with a stacked auto-encoder neural network will help obtain the dynamic data correlation or relationship between the prediction …

Original language	English
Pages (from-to)	446-454
Number of pages	9
Journal	Future Generation Computer Systems
Volume	89
Early online date	7 Jul 2018
DOIs	https://doi.org/10.1016/j.future.2018.06.052
Publication status	Published - Dec 2018

Bibliographical note

NOTICE: this is the author’s version of a work that was accepted for publication in Future Generation Computer Systems. Changes resulting from the publishing process, such as peer review, editing, corrections, structural formatting, and other quality control mechanisms may not be reflected in this document. Changes may have been made to this work since it was submitted for publication. A definitive version was subsequently published in Future Generation Computer Systems, [89], (2018)] DOI: 10.1016/j.future.2018.06.05

© 2017, Elsevier. Licensed under the Creative Commons Attribution-NonCommercial-NoDerivatives 4.0 International http://creativecommons.org/licenses/by-nc-nd/4.0/

Copyright © and Moral Rights are retained by the author(s) and/ or other copyright owners. A copy can be downloaded for personal non-commercial research or study, without prior permission or charge. This item cannot be reproduced or quoted extensively from without first obtaining permission in writing from the copyright holder(s). The content must not be changed in any way or sold commercially in any format or medium without the formal permission of the copyright holders.

Keywords

Stacked auto-encoder neural network
Association analysis
Sequence analysis
Dynamic data-driven application systems

Access to Document

10.1016/j.future.2018.06.052

Post-printAccepted author manuscript, 1.21 MBLicence: CC BY-NC-ND

Cite this

@article{a595202208f54db2ad3c2b0e09fe5dd2,

title = "Dynamic fine-tuning stacked auto-encoder neural network for weather forecast",

abstract = "With the advent of the big data era, dynamic and real-time data have increased in both volume and variety. It is difficult to make accurate predictions regarding data as they undergo rapid and dynamic changes. Autonomous cloud computing aims to reduce the time required for traditional machine learning. The stacked auto-encoder is a neural network approach in machine learning for feature extraction. It attempts to model high-level abstractions and to reduce data dimensions by using multiple processing layers. However, some common issues may occur during the implementation of deep learning or neural network models, such as over-complicated dimensions of the input data and difficulty in processing dynamic data. Therefore, combining the concept of dynamic data-driven system with a stacked auto-encoder neural network will help obtain the dynamic data correlation or relationship between the prediction …",

keywords = "Stacked auto-encoder neural network, Association analysis, Sequence analysis, Dynamic data-driven application systems",

author = "Szu-Yin Lin and Chiang, {Chi Chun} and Li, {Jung Bin} and Hung, {Zih Siang} and Kuo-Ming Chao",

note = "NOTICE: this is the author{\textquoteright}s version of a work that was accepted for publication in Future Generation Computer Systems. Changes resulting from the publishing process, such as peer review, editing, corrections, structural formatting, and other quality control mechanisms may not be reflected in this document. Changes may have been made to this work since it was submitted for publication. A definitive version was subsequently published in Future Generation Computer Systems, [89], (2018)] DOI: 10.1016/j.future.2018.06.05 {\textcopyright} 2017, Elsevier. Licensed under the Creative Commons Attribution-NonCommercial-NoDerivatives 4.0 International http://creativecommons.org/licenses/by-nc-nd/4.0/ Copyright {\textcopyright} and Moral Rights are retained by the author(s) and/ or other copyright owners. A copy can be downloaded for personal non-commercial research or study, without prior permission or charge. This item cannot be reproduced or quoted extensively from without first obtaining permission in writing from the copyright holder(s). The content must not be changed in any way or sold commercially in any format or medium without the formal permission of the copyright holders. ",

year = "2018",

month = dec,

doi = "10.1016/j.future.2018.06.052",

language = "English",

volume = "89",

pages = "446--454",

journal = "Future Generation Computer Systems",

issn = "0167-739X",

publisher = "Elsevier",

}

TY - JOUR

T1 - Dynamic fine-tuning stacked auto-encoder neural network for weather forecast

AU - Lin, Szu-Yin

AU - Chiang, Chi Chun

AU - Li, Jung Bin

AU - Hung, Zih Siang

AU - Chao, Kuo-Ming

N1 - NOTICE: this is the author’s version of a work that was accepted for publication in Future Generation Computer Systems. Changes resulting from the publishing process, such as peer review, editing, corrections, structural formatting, and other quality control mechanisms may not be reflected in this document. Changes may have been made to this work since it was submitted for publication. A definitive version was subsequently published in Future Generation Computer Systems, [89], (2018)] DOI: 10.1016/j.future.2018.06.05 © 2017, Elsevier. Licensed under the Creative Commons Attribution-NonCommercial-NoDerivatives 4.0 International http://creativecommons.org/licenses/by-nc-nd/4.0/ Copyright © and Moral Rights are retained by the author(s) and/ or other copyright owners. A copy can be downloaded for personal non-commercial research or study, without prior permission or charge. This item cannot be reproduced or quoted extensively from without first obtaining permission in writing from the copyright holder(s). The content must not be changed in any way or sold commercially in any format or medium without the formal permission of the copyright holders.

PY - 2018/12

Y1 - 2018/12

N2 - With the advent of the big data era, dynamic and real-time data have increased in both volume and variety. It is difficult to make accurate predictions regarding data as they undergo rapid and dynamic changes. Autonomous cloud computing aims to reduce the time required for traditional machine learning. The stacked auto-encoder is a neural network approach in machine learning for feature extraction. It attempts to model high-level abstractions and to reduce data dimensions by using multiple processing layers. However, some common issues may occur during the implementation of deep learning or neural network models, such as over-complicated dimensions of the input data and difficulty in processing dynamic data. Therefore, combining the concept of dynamic data-driven system with a stacked auto-encoder neural network will help obtain the dynamic data correlation or relationship between the prediction …

AB - With the advent of the big data era, dynamic and real-time data have increased in both volume and variety. It is difficult to make accurate predictions regarding data as they undergo rapid and dynamic changes. Autonomous cloud computing aims to reduce the time required for traditional machine learning. The stacked auto-encoder is a neural network approach in machine learning for feature extraction. It attempts to model high-level abstractions and to reduce data dimensions by using multiple processing layers. However, some common issues may occur during the implementation of deep learning or neural network models, such as over-complicated dimensions of the input data and difficulty in processing dynamic data. Therefore, combining the concept of dynamic data-driven system with a stacked auto-encoder neural network will help obtain the dynamic data correlation or relationship between the prediction …

KW - Stacked auto-encoder neural network

KW - Association analysis

KW - Sequence analysis

KW - Dynamic data-driven application systems

U2 - 10.1016/j.future.2018.06.052

DO - 10.1016/j.future.2018.06.052

M3 - Article

SN - 0167-739X

VL - 89

SP - 446

EP - 454

JO - Future Generation Computer Systems

JF - Future Generation Computer Systems

ER -

Dynamic fine-tuning stacked auto-encoder neural network for weather forecast

Abstract

Bibliographical note

Keywords

Access to Document

Fingerprint

Cite this