Conference Paper/Proceeding/Abstract 1283 views 1727 downloads
A Deep Learning Framework for Univariate Time Series Prediction Using Convolutional LSTM Stacked Autoencoders
2019 IEEE International Symposium on INnovations in Intelligent SysTems and Applications (INISTA), Pages: 1 - 6
Swansea University Author:
Cinzia Giannetti
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DOI (Published version): 10.1109/INISTA.2019.8778417
Abstract
This paper proposes a deep learning framework where wavelet transforms (WT), 2-dimensional Convolutional Neural Networks (CNNs) and Long Short-Term Memory (LSTM) stacked autoencoders (SAE) are combined towards single-step time series prediction. Within the framework, the input dataset is denoised us...
Published in: | 2019 IEEE International Symposium on INnovations in Intelligent SysTems and Applications (INISTA) |
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ISBN: | 978-1-7281-1863-5 978-1-7281-1862-8 |
Published: |
Sofia, Bulgaria
IEEE International Symposium on Innovations in Intelligent Systems and Applications (INISTA)
2019
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URI: | https://cronfa.swan.ac.uk/Record/cronfa51622 |
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2019-08-29T10:29:52.6831398 v2 51622 2019-08-29 A Deep Learning Framework for Univariate Time Series Prediction Using Convolutional LSTM Stacked Autoencoders a8d947a38cb58a8d2dfe6f50cb7eb1c6 0000-0003-0339-5872 Cinzia Giannetti Cinzia Giannetti true false 2019-08-29 MECH This paper proposes a deep learning framework where wavelet transforms (WT), 2-dimensional Convolutional Neural Networks (CNNs) and Long Short-Term Memory (LSTM) stacked autoencoders (SAE) are combined towards single-step time series prediction. Within the framework, the input dataset is denoised using wavelet decomposition, before learning in an unsupervised manner using SAEs comprising bidirectional Convolutional LSTM (ConvLSTM) layers to predict a single-step ahead value. To evaluate our proposed framework, we compared its performance to two (2) state-of-the-art deep learning predictive models using three open-source univariate time series datasets. The experimental results support the value of the approach when applied to univariate time series prediction. Conference Paper/Proceeding/Abstract 2019 IEEE International Symposium on INnovations in Intelligent SysTems and Applications (INISTA) 1 6 IEEE International Symposium on Innovations in Intelligent Systems and Applications (INISTA) Sofia, Bulgaria 978-1-7281-1863-5 978-1-7281-1862-8 29 7 2019 2019-07-29 10.1109/INISTA.2019.8778417 COLLEGE NANME Mechanical Engineering COLLEGE CODE MECH Swansea University 2019-08-29T10:29:52.6831398 2019-08-29T10:23:57.1407106 Faculty of Science and Engineering School of Aerospace, Civil, Electrical, General and Mechanical Engineering - Mechanical Engineering Aniekan Essien 1 Cinzia Giannetti 0000-0003-0339-5872 2 0051622-29082019102938.pdf essien2019.pdf 2019-08-29T10:29:38.6800000 Output 827281 application/pdf Accepted Manuscript true 2019-08-29T00:00:00.0000000 true eng |
title |
A Deep Learning Framework for Univariate Time Series Prediction Using Convolutional LSTM Stacked Autoencoders |
spellingShingle |
A Deep Learning Framework for Univariate Time Series Prediction Using Convolutional LSTM Stacked Autoencoders Cinzia Giannetti |
title_short |
A Deep Learning Framework for Univariate Time Series Prediction Using Convolutional LSTM Stacked Autoencoders |
title_full |
A Deep Learning Framework for Univariate Time Series Prediction Using Convolutional LSTM Stacked Autoencoders |
title_fullStr |
A Deep Learning Framework for Univariate Time Series Prediction Using Convolutional LSTM Stacked Autoencoders |
title_full_unstemmed |
A Deep Learning Framework for Univariate Time Series Prediction Using Convolutional LSTM Stacked Autoencoders |
title_sort |
A Deep Learning Framework for Univariate Time Series Prediction Using Convolutional LSTM Stacked Autoencoders |
author_id_str_mv |
a8d947a38cb58a8d2dfe6f50cb7eb1c6 |
author_id_fullname_str_mv |
a8d947a38cb58a8d2dfe6f50cb7eb1c6_***_Cinzia Giannetti |
author |
Cinzia Giannetti |
author2 |
Aniekan Essien Cinzia Giannetti |
format |
Conference Paper/Proceeding/Abstract |
container_title |
2019 IEEE International Symposium on INnovations in Intelligent SysTems and Applications (INISTA) |
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publishDate |
2019 |
institution |
Swansea University |
isbn |
978-1-7281-1863-5 978-1-7281-1862-8 |
doi_str_mv |
10.1109/INISTA.2019.8778417 |
publisher |
IEEE International Symposium on Innovations in Intelligent Systems and Applications (INISTA) |
college_str |
Faculty of Science and Engineering |
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facultyofscienceandengineering |
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Faculty of Science and Engineering |
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facultyofscienceandengineering |
hierarchy_parent_title |
Faculty of Science and Engineering |
department_str |
School of Aerospace, Civil, Electrical, General and Mechanical Engineering - Mechanical Engineering{{{_:::_}}}Faculty of Science and Engineering{{{_:::_}}}School of Aerospace, Civil, Electrical, General and Mechanical Engineering - Mechanical Engineering |
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description |
This paper proposes a deep learning framework where wavelet transforms (WT), 2-dimensional Convolutional Neural Networks (CNNs) and Long Short-Term Memory (LSTM) stacked autoencoders (SAE) are combined towards single-step time series prediction. Within the framework, the input dataset is denoised using wavelet decomposition, before learning in an unsupervised manner using SAEs comprising bidirectional Convolutional LSTM (ConvLSTM) layers to predict a single-step ahead value. To evaluate our proposed framework, we compared its performance to two (2) state-of-the-art deep learning predictive models using three open-source univariate time series datasets. The experimental results support the value of the approach when applied to univariate time series prediction. |
published_date |
2019-07-29T04:03:35Z |
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1763753292804915200 |
score |
11.021648 |