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Scientific time series and deep learning state of the art
| Date | Stars |
|---|---|
| 2026-07-31 | 281 |
| 2026-08-01 | 281 |
| 2026-08-06 | 281 |
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# Deep Learning and Time Series
This document shows a list of bibliographical references on DeepLearning and Time Series, organized by type and year. I add some additional notes on each reference.
Table of contents
* [Deef Belief Network with Restricted Boltzmann Machine](#deef-belief-network-with-restricted-boltzmann-machine)
- [Journal](#journal)
- [2017](#2017), [2016](#2016), [2014](#2014)
- [Conference](#conference)
- [2017](#2017-1), [2016](#2016-1), [2015](#2015)
* [Long short-term memory](#long-short-term-memory)
- [Journal](#journal-1)
- [2020](#2020), [2019](#2019), [2018](#2018), [2017](#2017-2)
- [Conference](#conference-1)
- [2019](#2019), [2018](#2018-1), [2017](#2017-3), [2016](#2016-2)
* [Auto-Encoders](#auto-encoders)
- [Journal](#journal-2)
- [2020](#2020), [2019](#2019), [2018](#2018), [2017](#2017-4), [2016](#2016-3)
- [Conference](#conference-2)
- [2019](#2019), [2018](#2018-1), [2017](#2017-3), [2016](#2016-4), [2015](#2015-1), [2013](#2013)
* [Convolutional neural network](#Convolutional-neural-network)
* [Combination of the above](#combination-of-the-above)
- [Journal](#journal-3)
- [2018](#2018-2), [2017](#2017-5)
- [Conference](#conference-3)
- [2017](#2017-6), [2016](#2016-5)
* [Others](#others)
- [2018](#2018-3), [2017](#2017-3), [2016](#2016-5), [2015](#2015-2), [2014](#2014-1)
* [Reviews](#reviews)
- [2017](#2017-4), [2014](#2014-2), [2012](#2012)
## Deef Belief Network with Restricted Boltzmann Machine
### Journal
#### 2017
- [Ryu, S., Noh, J., & Kim, H. (2017). Deep neural network based demand side short term load forecasting. Energies, 10(1), 3.](https://www.scopus.com/record/display.uri?eid=2-s2.0-85009236706&origin=resultslist&sort=plf-f&src=s&st1=deep+learning+time+series&nlo=&nlr=&nls=&sid=306771ADB79C2181330A84526BFB4363.wsnAw8kcdt7IPYLO0V48gA%3a210&sot=b&sdt=cl&cluster=scosubtype%2c%22ar%22%2ct&sl=40&s=TITLE-ABS-KEY%28deep+learning+time+series%29&relpos=4&citeCnt=0&searchTerm=)
Summary: The paper proposes deep neural network (DNN)-based load forecasting models and apply them to a demand side empirical load database. DNNs are trained in two different ways: a pre-training restricted Boltzmann machine and using ReLu without pre-training.
Notes:
- Model 1 train -> greedy layer-wise manner
- Model 1 Fine-tuning connection weights -> Back-propagation
- Model 2 train -> ReLu
- Model Sizes -> trial and error
- [Qiu, X., Ren, Y., Suganthan, P. N., & Amaratunga, G. A. (2017). Empirical Mode Decomposition based ensemble deep learning for load demand time series forecasting. Applied Soft Computing, 54, pages 246-255.](https://www.scopus.com/record/display.uri?eid=2-s2.0-85011866839&origin=resultslist&sort=plf-f&src=s&st1=deep+learning+time+series&st2=&sid=306771ADB79C2181330A84526BFB4363.wsnAw8kcdt7IPYLO0V48gA%3a10&sot=b&sdt=b&sl=40&s=TITLE-ABS-KEY%28deep+learning+time+series%29&relpos=0&citeCnt=0&searchTerm=)
Summary: In this paper a Deep Belief Network (DBN) including two restricted Boltzmann machines (RBMs) was used to model load demand series.
#### 2016
- [Hirata, T.a, Kuremoto, T.a, Obayashi, M.a, Mabu, S.a, Kobayashi, K.b (2016). A novel approach to time series forecasting using deep learning and linear model. IEEJ Transactions on Electronics, Information and Systems, 136(3), pages 348-356.](https://www.researchgate.net/publication/296472323/download)
Summary: This paper presents a hybrid prediction method using DBNs (deep Belief Network) and ARIMA.
#### 2014
- [Takashi Kuremotoa, Shinsuke Kimuraa, Kunikazu Kobayashib, Masanao Obayashia (2014).Time series forecasting using a deep belief network with restricted Boltzmann machines. Neurocomputing, 137(5), pages 47–56](http://www.sciencedirect.com/science/article/pii/S0925231213007388)
Summary: This papersExcerpt of 30,639 characters
Read on GitHubWould you bet a product on this? Bounded 0–100 and slow moving.
matched fp:1f8789c303ced6a6, llm:Repository topics: deep-learning, papers, time-series. Description and README: "list of bibliographical references on DeepLearning and Time Series, organized by type and year."