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.
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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:
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Model 1 train -> greedy layer-wise manner
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Model 1 Fine-tuning connection weights -> Back-propagation
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Model 2 train -> ReLu
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Model Sizes -> trial and error
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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.
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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)
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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.
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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.
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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:
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Model train -> greedy layer-wise manner
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Fine-tuning connection weights -> Back-propagation
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Model Sizes -> several ccombinations
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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.
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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.
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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.
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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.
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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.
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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.
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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).
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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:
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Model Train -> greedy layer-wise manner
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Top layer -> logistic regression
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Fine-tuning connection weights -> Back-propagation
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Model sizes -> several ccombinations
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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.
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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.
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