Deef Belief Network with Restricted Boltzmann Machine

2017

• Ryu, S., Noh, J., & Kim, H. (2016). Deep neural network based demand side short term load forecasting. Energies, 10(1), 3.

  Notes:

  • Model 1 train -> greedy layer-wise manner
  • Model 1 Fine-tuning connection weights -> Back-propagation
  • Model 2 train -> ReLu
  • 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, 246-255.

2016 -> A novel approach to time series forecasting using deep learning and linear model

2016 -> Deep Belief Network Using Reinforcement Learning and Its Applications to Time Series Forecasting

2016 -> Time series prediction for evolutions of complex systems: A deep learning approach
Top layer -> SVM
Fine-tuning connection weights -> Back-propagation

2016 -> Traffic speed prediction using deep learning method
Train -> greedy layer-wise manner
Fine-tuning connection weights -> Back-propagation
Sizes -> several ccombinations

2015 -> Ensemble deep learning for regression and time series forecasting
Top layer -> support vector regression (SVR)

2014 -> Time series forecasting using a deep belief network with restricted Boltzmann machines
Train -> greedy layer-wise manner
Fine-tuning connection weights -> Back-propagation
Sizes and learning rates -> PSO

Long short-term memory

2017 -> LSTM network: a deep learning approach for short-term traffic forecast

2016 -> Sequence-to-Sequence Model with Attention for Time Series Classification

2016 -> Deep learning for stock prediction using numerical and textual information

2016 -> Travel time prediction with LSTM neural network

2016 -> Building energy load forecasting using Deep Neural Networks
Model train -> Backpropagation

2016 -> Traffic flow prediction with Long Short-Term Memory Networks (LSTMs)

2016 -> Deep neural network architectures for forecasting analgesic response

2016 -> Long short-term memory model for traffic congestion prediction with online open data
Sizes and learning rates -> several ccombinations

Auto-Encoders

2016 -> Deep learning architecture for air quality predictions
Train -> greedy layer-wise manner
Top layer -> logistic regression
Fine-tuning connection weights -> Back-propagation
Sizes -> several ccombinations

2016 -> Rainfall Prediction: A Deep Learning Approach
Top layer -> multilayer perceptron
Sizes and learning rates -> several combinations

2016 -> Optimized Structure of the Traffic Flow Forecasting Model With a Deep Learning Approach
Fine-tuning connection weights -> Levenberg-Marquadt

2015 -> Forecasting the weather of Nevada: A deep learning approach
Top layer -> feed-forward neural network

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)

Deef Belief Network with Restricted Boltzmann Machine - Auto-Encoders

2016 -> Deep Learning for Wind Speed Forecasting in Northeastern Region of Brazil
Train -> greedy layer-wise manner
Fine-tuning connection weights -> Levenberg-Marquadt
Sizes -> several combinations

Long Short-Term Memory - Deef Belief Network with Restricted Boltzmann Machine - AutoEncoders Long Short-Term Memory

2016 -> Deep Learning for solar power forecasting — An approach using AutoEncoder and LSTM Neural Networks

Others

2017 -> Convolutional neural networks for time series classification
Type -> Convolutional neural network

2017 -> Short term power load forecasting using Deep Neural Networks
Type -> Recurrent neural network

2016 -> Deep Convolutional Factor Analyser for Multivariate Time Series Modeling
Type -> Convolutional neural network

2016 -> A Deep Learning Approach for the Prediction of Retail Store Sales
Type -> Not specified

2016 -> Optimization of decentralized renewable energy system by weather forecasting and deep machine learning techniques
Type -> a novel optimization tool platform using Boltzmann machine algorithm for NMIP

2015 -> Weather forecasting using deep learning techniques
Type -> Recurrent neural network, convolutional neural network

2014 -> Time Series Classification Using Multi-Channels Deep Convolutional Neural Networks
Type -> Multi-Channels Deep Convolution Neural Networks

Reviews

2017 -> Deep Learning for Time-Series Analysis

2014 -> A review of unsupervised feature learning and deep learning for time-series modeling

2012 -> Deep Learning for Time Series Modeling