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Deep Learning (DL) models can be used to tackle time series analysis tasks with great success.
Financial time series forecasting using support vector machines
Kyoung-jae Kim · 2003
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Adaptive normalization: A novel data normalization approach for non-stationary time series
Eduardo Ogasawara, Leonardo C Martinez, Daniel De Oliveira, Geraldo Zimbrão, Gisele L Pappa, and Marta Mattoso · 2010
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Deep sparse rectifier neural networks
Xavier Glorot, Antoine Bordes, and Yoshua Bengio · 2011
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Individual household electric power consumption data set
G Hébrail and A Bérard · 2012
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RMSProp: Divide the gradient by a running average of its recent magnitude
Tijmen Tieleman and Geoffrey Hinton · 2012
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Toward automatic time-series forecasting using neural networks
Weizhong Yan · 2012
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Developing a local least-squares support vector machines-based neuro-fuzzy model for nonlinear and chaotic time series prediction
Arash Miranian and Majid Abdollahzade · 2013
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Empirical evaluation of gated recurrent neural networks on sequence modeling
Junyoung Chung, Caglar Gulcehre, KyungHyun Cho, and Yoshua Bengio · 2014
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Time series forecasting using a deep belief network with restricted boltzmann machines
Takashi Kuremoto, Shinsuke Kimura, Kunikazu Kobayashi, and Masanao Obayashi · 2014
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Impact of data normalization on stock index forecasting
SC Nayak, BB Misra, and HS Behera · 2014
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Dropout: a simple way to prevent neural networks from overfitting
Nitish Srivastava, Geoffrey Hinton, Alex Krizhevsky, Ilya Sutskever, and Ruslan Salakhutdinov · 2014
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Batch normalization: Accelerating deep network training by reducing internal covariate shift
Sergey Ioffe and Christian Szegedy · 2015
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Modelling high-frequency limit order book dynamics with support vector machines
Alec N. Kercheval and Yuan Zhang · 2015
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Self-normalization for time series: a review of recent developments
Xiaofeng Shao · 2015
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Two machine learning approaches for short-term wind speed time-series prediction
Ronay Ak, Olga Fink, and Enrico Zio · 2016
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Jimmy Lei Ba, Jamie Ryan Kiros, and Geoffrey E Hinton · 2016
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Multi-scale convolutional neural networks for time series classification
A deep machine learning method for classifying cyclic time series of biological signals using time-growing neural network
Arash Gharehbaghi and Maria Lindén · 2018
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Milla Mäkinen, Juho Kanniainen, Moncef Gabbouj, and Alexandros Iosifidis · 2018
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Machine learning for forecasting mid price movement using limit order book data
Paraskevi Nousi, Avraam Tsantekidis, Nikolaos Passalis, Adamantios Ntakaris, Juho Kanniainen, Anastasios Tefas, Moncef Gabbouj, and Alexandros Iosifidis · 2018
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Benchmark dataset for mid-price prediction of limit order book data
Adamantios Ntakaris, Martin Magris, Juho Kanniainen, Moncef Gabbouj, and Alexandros Iosifidis · 2018
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Training lightweight deep convolutional neural networks using bag-of-features pooling
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Zhicheng Cui, Wenlin Chen, and Yixin Chen · 2016
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Deep direct reinforcement learning for financial signal representation and trading
Yue Deng, Feng Bao, Youyong Kong, Zhiquan Ren, and Qionghai Dai · 2017
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Lstm: A search space odyssey
Klaus Greff, Rupesh K Srivastava, Jan Koutník, Bas R Steunebrink, and Jürgen Schmidhuber · 2017
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Arbitrary style transfer in real-time with adaptive instance normalization
Xun Huang and Serge J Belongie · 2017
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Forecasting stock prices from the limit order book using convolutional neural networks
Avraam Tsantekidis, Nikolaos Passalis, Anastasios Tefas, Juho Kanniainen, Moncef Gabbouj, and Alexandros Iosifidis · 2017
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Using deep learning to detect price change indications in financial markets
Avraam Tsantekidis, Nikolaos Passalis, Anastasios Tefas, Juho Kanniainen, Moncef Gabbouj, and Alexandros Iosifidis · 2017
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Nikolaos Passalis and Anastasios Tefas · 2018
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Temporal bag-of-features learning for predicting mid price movements using high frequency limit order book data
Nikolaos Passalis, Anastasios Tefas, Juho Kanniainen, Moncef Gabbouj, and Alexandros Iosifidis · 2018
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Temporal attention-augmented bilinear network for financial time-series data analysis
Dat Thanh Tran, Alexandros Iosifidis, Juho Kanniainen, and Moncef Gabbouj · 2018
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Using deep learning for price prediction by exploiting stationary limit order book features
Avraam Tsantekidis, Nikolaos Passalis, Anastasios Tefas, Juho Kanniainen, Moncef Gabbouj, and Alexandros Iosifidis · 2018
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Group normalization, 2018
Yuxin Wu and Kaiming He · 2018
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Machine learning for forecasting mid-price movements using limit order book data
Paraskevi Nousi, Avraam Tsantekidis, Nikolaos Passalis, Adamantios Ntakaris, Juho Kanniainen, Anastasios Tefas, Moncef Gabbouj, and Alexandros Iosifidis · 2019
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Nikolaos Passalis, Anastasios Tefas, Juho Kanniainen, Moncef Gabbouj, and Alexandros Iosifidis · 2019
Closest in time.