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
  * 2017 * 2016 * 2015 * 2014
• Long short-term memory * 2017 * 2016
• Auto-Encoders * 2016 * 2015 * 2013
• Deef Belief Network with Restricted Boltzmann Machine - Auto-Encoders * 2016
• Long Short-Term Memory - Deef Belief Network with Restricted Boltzmann Machine - AutoEncoders Long Short-Term Memory * 2016
• Others * 2017 * 2016 * 2015 * 2014
• Reviews * 2017 * 2014 * 2012

Deef Belief Network with Restricted Boltzmann Machine

2017

• Ryu, S., Noh, J., & Kim, H. (2017). Deep neural network based demand side short term load forecasting. Energies, 10(1), 3.

  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.

  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.

  Summary: This paper presents a hybrid prediction method using DBNs (deep Belief Network) and ARIMA. (without access to full paper)

• Takaomi Hirata, Takashi Kuremoto, Masanao Obayashi, Shingo Mabu, Kunikazu Kobayashi (2016).Deep Belief Network Using Reinforcement Learning and Its Applications to Time Series Forecasting. International Conference on Neural Information Processing

  Summary: This paper introduces a reinforcement learning method named stochastic gradient ascent (SGA) to the DBN with RBMs instead conventional BackPropagation to predict a benchmark named CATS data.

• Peng Jiang, Cheng Chen, Xiao Liu (2016). Time series prediction for evolutions of complex systems: A deep learning approach. Control and Robotics Engineering (ICCRE), 2016 IEEE International Conference on

  Summary: The paper proposes a deep learning approach, which hybridizes a deep belief networks (DBNs) and a nonlinear kernel-based parallel evolutionary SVM (ESVM), to predict evolution states of complex systems in a classification manner.

  Notes:

  • Top layer -> SVM
  • Fine-tuning connection weights -> Back-propagation

• Yuhan Jia; Jianping Wu; Yiman Du (2016). Traffic speed prediction using deep learning method. Intelligent Transportation Systems (ITSC), 2016 IEEE 19th International Conference on

  Summary: In this paper, a deep learning method, the Deep Belief Network (DBN) model, is proposed for short-term traffic speed information prediction.

  Notes:

  • Model train -> greedy layer-wise manner
  • Fine-tuning connection weights -> Back-propagation
  • Model Sizes -> several ccombinations

2015

• Xueheng Qiu; Le Zhang; Ye Ren; P. N. Suganthan; Gehan Amaratunga (2015). Ensemble deep learning for regression and time series forecasting. Computational Intelligence in Ensemble Learning (CIEL), 2014 IEEE Symposium on

  Summary: This paper proposes an ensemble of deep learning belief networks (DBN) for regression and time series forecasting on electricity load demand datasets. Another contribution is to aggregate the outputs from various DBNs by a support vector regression (SVR) model.

  Notes:

  • Top layer -> support vector regression (SVR)

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

  Summary: This papers proposes a method for time series prediction using deep belief nets (DBN) (with 3-layer of RBMs to capture the feature of input space of time series data).

  Notes:

  • Mode Train -> greedy layer-wise manner
  • Fine-tuning connection weights -> Back-propagation
  • Mode sizes and learning rates -> PSO

Long short-term memory

2017

• Zheng Zhao; Weihai Chen; Xingming Wu; Peter C. Y. Chen; Jingmeng Liu (2017). LSTM network: a deep learning approach for short-term traffic forecast. IET Intelligent Transport Systems, 11(2), 3, pages 68 - 75

  Summary: This paper pses a traffic forecast model based on long short-term memory (LSTM) network, that considers temporal-spatial correlation in traffic system via a two-dimensional network which is composed of many memory units.

2016

• Yujin Tang; Jianfeng Xu; Kazunori Matsumoto; Chihiro Ono (2016). Sequence-to-Sequence Model with Attention for Time Series Classification. Data Mining Workshops (ICDMW), 2016 IEEE 16th International Conference on.

  Summary: The paper proposes a model incorporating a sequence-to-sequence model that consists two LSTMs, one encoder and one decoder. The encoder LSTM accepts input time series, extracts information and based on which the decoder LSTM constructs fixed length sequences that can be regarded as discriminatory features. The paper also introduces the attention mechanism.

• Ryo Akita; Akira Yoshihara; Takashi Matsubara; Kuniaki Uehara (2016. Deep learning for stock prediction using numerical and textual information. Computer and Information Science (ICIS), 2016 IEEE/ACIS 15th International Conference on.

  Summary: This paper proposes an application of deep learning models, Paragraph Vector, and Long Short-Term Memory (LSTM), to financial time series forecasting.

• Yanjie Duan; Yisheng Lv; Fei-Yue Wang (2016). Travel time prediction with LSTM neural network. Intelligent Transportation Systems (ITSC), 2016 IEEE 19th International Conference on.

  Summary: This paper explores a deep learning model, the LSTM neural network model, for travel time prediction. By employing the travel time data provided by Highways England dataset, the paper construct 66 series prediction LSTM neural networks.

• Daniel L. Marino; Kasun Amarasinghe; Milos Manic (2016) .Building energy load forecasting using Deep Neural Networks. Industrial Electronics Society , IECON 2016 - 42nd Annual Conference of the IEEE.

  Summary: This paper presents an energy load forecasting methodology based on Deep Neural Networks (Long Short Term Memory (LSTM) algorithms). The presented work investigates two LSTM based architectures: 1) standard LSTM and 2) LSTM-based Sequence to Sequence (S2S) architecture. Both methods were implemented on a benchmark data set of electricity consumption data from one residential customer.

  Notes:

  • Model train -> Backpropagation

• Hongxin Shao; Boon-Hee Soong (2016). Traffic flow prediction with Long Short-Term Memory Networks (LSTMs). Region 10 Conference (TENCON), 2016 IEEE.

  Summary: This paper explores the application of Long Short-Term Memory Networks (LSTMs) in short-term traffic flow prediction.

• Paul Nickerson; Patrick Tighe; Benjamin Shickel; Parisa Rashidi (2016). Deep neural network architectures for forecasting analgesic response. Engineering in Medicine and Biology Society (EMBC), 2016 IEEE 38th Annual International Conference of the.

  Summary: This paper compares conventional machine learning methods with modern neural network architectures to better forecast analgesic responses. The paper applies the LSTM to predict what the next measured pain score will be after administration of an analgesic drug, and compared the results with simpler techniques.

• Yuan-yuan Chen; Yisheng Lv; Zhenjiang Li; Fei-Yue Wang (2016). Long short-term memory model for traffic congestion prediction with online open data. Intelligent Transportation Systems (ITSC), 2016 IEEE 19th International Conference on.

  Summary: This paper uses a stacked long short-term memory model to learn and predict the patterns of traffic conditions (that are collected from online open web based map services).

  Notes:

  • Model sizes and learning rates -> several ccombinations

Auto-Encoders

2016

• Li, X.ac, Peng, L.a, Hu, Y.ac, Shao, J.b, Chi, T.a (2016). Deep learning architecture for air quality predictions. Environmental Science and Pollution Research. 23(22), pages 22408-22417

  Summary: This paper proposed a novel spatiotemporal deep learning (STDL)-based air quality prediction method that inherently considers spatial and temporal correlations. A stacked autoencoder (SAE) model is used to extract inherent air quality features.

  Notes:

  • Model Train -> greedy layer-wise manner
  • Top layer -> logistic regression
  • Fine-tuning connection weights -> Back-propagation
  • Model sizes -> several ccombinations

• Emilcy Hernández, Victor Sanchez-Anguix, Vicente Julian, Javier Palanca, Néstor Duque (2016). Rainfall Prediction: A Deep Learning Approach. International Conference on Hybrid Artificial Intelligence Systems.

  Summary: The paper introduces an architecture based on Deep Learning for the prediction of the accumulated daily precipitation for the next day. More specifically, it includes an autoencoder for reducing and capturing non-linear relationships between attributes, and a multilayer perceptron for the prediction task.

  Notes:

  • Top layer -> multilayer perceptron
  • Model sizes and learning rates -> several combinations

• Hao-Fan Yang, Tharam S. Dillon (2016). Optimized Structure of the Traffic Flow Forecasting Model With a Deep Learning Approach. IEEE Transactions on Neural Networks and Learning Systems

  Summary: This paper proposes a stacked autoencoder Levenberg–Marquardt model to improve forecasting accuracy. It is applied to real-world data collected from the M6 freeway in the U.K.

  Notes:

  • Fine-tuning connection weights -> Levenberg-Marquadt

2015

• Moinul Hossain; Banafsheh Rekabdar; Sushil J. Louis; Sergiu Dascalu (2015). Forecasting the weather of Nevada: A deep learning approach. Neural Networks (IJCNN), 2015 International Joint Conference on.

  Summary: This paper compares a deep learning network (Stacked Denoising Auto-Encoders (SDAE)) against a standard neural network for predicting air temperature from historical pressure, humidity, and temperature data gathered from meteorological sensors in Northwestern Nevada. In addition, predicting air temperature from historical air temperature data alone can be improved by employing related weather variables like barometric pressure, humidity and wind speed data in the training process.

  Notes:

  • Top layer -> feed-forward neural network

2013

• Pablo Romeu, Francisco Zamora-Martínez, Paloma Botella-Rocamora, Juan Pardo (2013). Time-Series Forecasting of Indoor Temperature Using Pre-trained Deep Neural Network. International Conference on Artificial Neural Networks.

  Summary: This paper presents a study of deep learning techniques (Stacked Denoising Auto-Encoders (SDAEs)) applied to time-series forecasting in a real indoor temperature forecasting task.

Deef Belief Network with Restricted Boltzmann Machine - Auto-Encoders

2016

• Anderson Tenório Sergio; Teresa B. Ludermir (2016). Deep Learning for Wind Speed Forecasting in Northeastern Region of Brazil. Intelligent Systems (BRACIS), 2015 Brazilian Conference on.

  Summary: This work aims to investigate the use of some of deep learning architectures (deep belief networks and aunto-encoders) in predicting the hourly average speed of winds in the Northeastern region of Brazil.

  Notes:

  • Model Train -> greedy layer-wise manner
  • Fine-tuning connection weights -> Levenberg-Marquadt
  • Model sizes -> several combinations

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

2016

• André Gensler; Janosch Henze; Bernhard Sick; Nils Raabe (2016). Deep Learning for solar power forecasting — An approach using AutoEncoder and LSTM Neural Networks. Systems, Man, and Cybernetics (SMC), 2016 IEEE International Conference on.

  Summary: This paper introduces different Deep Learning and Artificial Neural Network algorithms, such as Deep Belief Networks, AutoEncoder, and LSTM in the field of renewable energy power forecasting of 21 solar power plants.

Others

2017

• Bendong Zhao; Huanzhang Lu; Shangfeng Chen; Junliang Liu; Dongya Wu (2017). Convolutional neural networks for time series classification. Journal of Systems Engineering and Electronics. 28(1), pages 162.169)

  Summary: This paper proposes a convolutional neural network (CNN) framework for time series classification. Two groups of experiments are conducted on simulated data sets and eight groups of experiments are conducted on real-world data sets from different application domains.

  Type: Convolutional neural network

• Ghulam Mohi Ud Din; Angelos K. Marnerides (2017). Short term power load forecasting using Deep Neural Networks. Computing, Networking and Communications (ICNC), 2017 International Conference on.

  Summary: This paper exploits the applicability of and compares the performance of the Feed-forward Deep Neural Network (FF-DNN) and Recurrent Deep Neural Network (R-DNN) models on the basis of accuracy and computational performance in the context of time-wise short term forecast of electricity load. The herein proposed method is evaluated over real datasets gathered in a period of 4 years and provides forecasts on the basis of days and weeks ahead.

  Type: Recurrent neural network

2016

• Chao Yuan; Amit Chakraborty (2016). Deep Convolutional Factor Analyser for Multivariate Time Series Modeling. Data Mining (ICDM), 2016 IEEE 16th International Conference on.

  Summary: The paper presents a deep convolutional factor analyser (DCFA) for multivariate time series modeling. The network is constructed in a way that bottom layer nodes are independent. Through a process of up-sampling and convolution, higher layer nodes gain more temporal dependency.

  Type: Convolutional neural network

• Yuta Kaneko; Katsutoshi Yada (2016). A Deep Learning Approach for the Prediction of Retail Store Sales. Data Mining Workshops (ICDMW), 2016 IEEE 16th International Conference on.

  Summary: The present study uses three years' worth of point-of-sale (POS) data from a retail store to construct a sales prediction model that, given the sales of a particular day, predicts the changes in sales on the following day.

  Type: Not specified

• Tomah Sogabe; Haruhisa Ichikawa; Tomah Sogabe; Katsuyoshi Sakamoto; Koichi Yamaguchi; Masaru Sogabe; Takashi Sato; Yusuke Suwa (2016). Optimization of decentralized renewable energy system by weather forecasting and deep machine learning techniques. Innovative Smart Grid Technologies - Asia (ISGT-Asia), 2016 IEEE.

  Summary: This work reports on employing the deep learning artificial intelligence techniques to predict the energy consumption and power generation together with the weather forecasting numerical simulation. An optimization tool platform using Boltzmann machine algorithm for NMIP problem is also proposed for better computing scalable decentralized renewable energy system.

  Type: a novel optimization tool platform using Boltzmann machine algorithm for NMIP

2015

• Afan Galih Salman; Bayu Kanigoro; Yaya Heryadi (2015). Weather forecasting using deep learning techniques. Advanced Computer Science and Information Systems (ICACSIS), 2015 International Conference on.

  Summary: This study investigates deep learning techniques for weather forecasting. In particular, this study will compare prediction performance of Recurrence Neural Network (RNN), Conditional Restricted Boltzmann Machine (CRBM), and Convolutional Network (CN) models. Those models are tested using weather dataset which are collected from a number of weather stations.

  Type: Recurrent neural network, convolutional neural network

• Mladen Dalto (2015). Deep neural networks for time series prediction with applications in ultra-short-term wind forecasting

  Summary: The aim of this paper is to present deep neural network architectures and algorithms and explore their use in time series prediction. Shallow and deep neural networks coupled with two input variable selection algorithms are compared on a ultra-short-term wind prediction task.

  Type: MultiLayer Perceptron.

2014

• Yi ZhengQi LiuEnhong ChenYong GeJ. Leon Zhao. (2014). Time Series Classification Using Multi-Channels Deep Convolutional Neural Networks. International Conference on Web-Age Information Management.

  Summary: This paper explores the feature learning techniques to improve the performance of traditional feature-based approaches. Specifically, the paper proposes a deep learning framework for multivariate time series classification in two groups of experiments on real-world data sets from different application domains.

  Type: Multi-Channels Deep Convolution Neural Networks

• Dmitry Vengertsev (2014). Deep Learning Architecture for Univariate Time Series Forecasting

  Summary: This paper overviews the particular challenges present in applying Conditional Restricted Boltzmann Machines (CRBM) to univariate time-series forecasting and provides a comparison to common algorithms used for time-series prediction. As a benchmark dataset for testing and comparison of forecasting algorithms, the paper selected M3 competition dataset.

  Type: Conditional Restricted Boltzmann Machines (CRBM)

Reviews

2017

• John Cristian Borges Gamboa 2017. Deep Learning for Time-Series Analysis

  Summary: This paper presents review of the main Deep Learning techniques, and some applications on Time-Series analysis are summaried.

2014

• Martin Längkvist, Lars Karlsson, Amy Loutfi (2014). A review of unsupervised feature learning and deep learning for time-series modeling. Pattern Recognition Letters. 42(1), pages 11–24.

  Summary: This paper gives a review of the recent developments in deep learning and unsupervised feature learning for time-series problems.

2012

• Enzo Busseti, Ian Osband, Scott Wong (2012). Deep Learning for Time Series Modeling