└── README.md /README.md: -------------------------------------------------------------------------------- 1 | 2 | ## Deef Belief Network with Restricted Boltzmann Machine 3 | 4 | ### 2017 5 | 6 | - [Ryu, S., Noh, J., & Kim, H. (2016). 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=) 7 | 8 | Notes: 9 | - Model 1 train -> greedy layer-wise manner 10 | - Model 1 Fine-tuning connection weights -> Back-propagation 11 | - Model 2 train -> ReLu 12 | - Sizes -> trial and error 13 | 14 | - [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, 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=) 15 | 16 | 2016 -> [A novel approach to time series forecasting using deep learning and linear model](https://www.scopus.com/record/display.uri?eid=2-s2.0-84960451045&origin=resultslist&sort=r-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=3&citeCnt=0&searchTerm=)
17 | 18 | 2016 -> [Deep Belief Network Using Reinforcement Learning and Its Applications to Time Series Forecasting](http://link.springer.com/chapter/10.1007/978-3-319-46675-0_4)
19 | 20 | 2016 -> [Time series prediction for evolutions of complex systems: A deep learning approach](http://ieeexplore.ieee.org/document/7476150/)
21 | Top layer -> SVM
22 | Fine-tuning connection weights -> Back-propagation
23 | 24 | 2016 -> [Traffic speed prediction using deep learning method](http://ieeexplore.ieee.org/document/7795712/)
25 | Train -> greedy layer-wise manner
26 | Fine-tuning connection weights -> Back-propagation
27 | Sizes -> several ccombinations
28 | 29 | 2015 -> [Ensemble deep learning for regression and time series forecasting](http://ieeexplore.ieee.org/abstract/document/7015739/)
30 | Top layer -> support vector regression (SVR)
31 | 32 | 2014 -> [Time series forecasting using a deep belief network with restricted Boltzmann machines](http://www.sciencedirect.com/science/article/pii/S0925231213007388)
33 | Train -> greedy layer-wise manner
34 | Fine-tuning connection weights -> Back-propagation
35 | Sizes and learning rates -> PSO
36 | 37 | ## Long short-term memory 38 | 39 | 2017 -> [LSTM network: a deep learning approach for short-term traffic forecast](http://ieeexplore.ieee.org/document/7874313/)
40 | 41 | 2016 -> [Sequence-to-Sequence Model with Attention for Time Series Classification](http://ieeexplore.ieee.org/document/7836709/)
42 | 43 | 2016 -> [Deep learning for stock prediction using numerical and textual information](http://ieeexplore.ieee.org/document/7550882/)
44 | 45 | 2016 -> [Travel time prediction with LSTM neural network](http://ieeexplore.ieee.org/document/7795686/)
46 | 47 | 2016 -> [Building energy load forecasting using Deep Neural Networks](http://ieeexplore.ieee.org/document/7793413/)
48 | Model train -> Backpropagation
49 | 50 | 2016 -> [Traffic flow prediction with Long Short-Term Memory Networks (LSTMs)](http://ieeexplore.ieee.org/document/7848593/)
51 | 52 | 2016 -> [Deep neural network architectures for forecasting analgesic response](http://ieeexplore.ieee.org/document/7591352/)
53 | 54 | 2016 -> [Long short-term memory model for traffic congestion prediction with online open data](http://ieeexplore.ieee.org/document/7795543/)
55 | Sizes and learning rates -> several ccombinations
56 | 57 | ## Auto-Encoders 58 | 59 | 2016 -> [Deep learning architecture for air quality predictions](https://www.scopus.com/record/display.uri?eid=2-s2.0-84991071427&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=9&citeCnt=0&searchTerm=)
60 | Train -> greedy layer-wise manner
61 | Top layer -> logistic regression
62 | Fine-tuning connection weights -> Back-propagation
63 | Sizes -> several ccombinations
64 | 65 | 2016 -> [Rainfall Prediction: A Deep Learning Approach](http://link.springer.com/chapter/10.1007/978-3-319-32034-2_13)
66 | Top layer -> multilayer perceptron
67 | Sizes and learning rates -> several combinations
68 | 69 | 2016 -> [Optimized Structure of the Traffic Flow Forecasting Model With a Deep Learning Approach](http://ieeexplore.ieee.org/stamp/stamp.jsp?tp=&arnumber=7517319)
70 | Fine-tuning connection weights -> Levenberg-Marquadt
71 | 72 | 2015 -> [Forecasting the weather of Nevada: A deep learning approach](http://ieeexplore.ieee.org/document/7280812/)
73 | Top layer -> feed-forward neural network
74 | 75 | 2013 -> Time-Series Forecasting of Indoor Temperature Using Pre-trained Deep Neural Network](http://link.springer.com/chapter/10.1007/978-3-642-40728-4_57)
76 | 77 | ## Deef Belief Network with Restricted Boltzmann Machine - Auto-Encoders 78 | 79 | 2016 -> [Deep Learning for Wind Speed Forecasting in Northeastern Region of Brazil](http://ieeexplore.ieee.org/document/7424040/)
80 | Train -> greedy layer-wise manner
81 | Fine-tuning connection weights -> Levenberg-Marquadt
82 | Sizes -> several combinations
83 | 84 | ## Long Short-Term Memory - Deef Belief Network with Restricted Boltzmann Machine - AutoEncoders Long Short-Term Memory 85 | 86 | 2016 -> [Deep Learning for solar power forecasting — An approach using AutoEncoder and LSTM Neural Networks]( http://ieeexplore.ieee.org/document/7844673/)
87 | 88 | ## Others 89 | 90 | 2017 -> [Convolutional neural networks for time series classification](http://ieeexplore.ieee.org/document/7870510/)
91 | Type -> Convolutional neural network
92 | 93 | 2017 -> [Short term power load forecasting using Deep Neural Networks](http://ieeexplore.ieee.org/document/7876196/)
94 | Type -> Recurrent neural network
95 | 96 | 2016 -> [Deep Convolutional Factor Analyser for Multivariate Time Series Modeling](http://ieeexplore.ieee.org/document/7837993/)
97 | Type -> Convolutional neural network
98 | 99 | 2016 -> [A Deep Learning Approach for the Prediction of Retail Store Sales](http://ieeexplore.ieee.org/document/7836713/)
100 | Type -> Not specified
101 | 102 | 2016 -> [Optimization of decentralized renewable energy system by weather forecasting and deep machine learning techniques](http://ieeexplore.ieee.org/document/7796524/)
103 | Type -> a novel optimization tool platform using Boltzmann machine algorithm for NMIP
104 | 105 | 2015 -> [Weather forecasting using deep learning techniques](http://ieeexplore.ieee.org/document/7415154/)
106 | Type -> Recurrent neural network, convolutional neural network
107 | 108 | 2014 -> [Time Series Classification Using Multi-Channels Deep Convolutional Neural Networks](http://link.springer.com/chapter/10.1007/978-3-319-08010-9_33)
109 | Type -> Multi-Channels Deep Convolution Neural Networks
110 | 111 | 112 | ## Reviews 113 | 114 | 2017 -> [Deep Learning for Time-Series Analysis](https://arxiv.org/abs/1701.01887)
115 | 116 | 2014 -> [A review of unsupervised feature learning and deep learning for time-series modeling](http://www.sciencedirect.com/science/article/pii/S0167865514000221)
117 | 118 | 2012 -> [Deep Learning for Time Series Modeling](https://pdfs.semanticscholar.org/a241/a7e26d6baf2c068601813216d3cc09e845ff.pdf)
119 | --------------------------------------------------------------------------------