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Financial time-series forecasting is one of the most challenging domains in the field of time-series analysis.
“Gradient-based learning applied to document recognition,”
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Earlier work this paper cites.
Recurrent neural networks for prediction: learning algorithms, architectures and stability
Danilo Mandic and Jonathon Chambers, · 2001
Earlier work this paper cites.
“Statistical properties of stock order books: empirical results and models,”
Jean-Philippe Bouchaud, Marc Mézard, and Marc Potters, · 2002
Earlier work this paper cites.
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Earlier work this paper cites.
“Neural machine translation by jointly learning to align and translate,”
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Earlier work this paper cites.
“Temporal attention-augmented bilinear network for financial time-series data analysis,”
Dat Thanh Tran, Alexandros Iosifidis, Juho Kanniainen, and Moncef Gabbouj, · 2017
Earlier work this paper cites.
Ashish Vaswani, Noam Shazeer, Niki Parmar, Jakob Uszkoreit, Llion Jones, Aidan N Gomez, Lukasz Kaiser, and Illia Polosukhin, · 2017
Earlier work this paper cites.
“A dual-stage attention-based recurrent neural network for time series prediction,”
Yao Qin, Dongjin Song, Haifeng Chen, Wei Cheng, Guofei Jiang, and Garrison Cottrell, · 2017
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“Temporal bag-of-features learning for predicting mid price movements using high frequency limit order book data,”
N. Passalis, A. Tefas, J. Kanniainen, M. Gabbouj, and A. Iosifidis, · 2018
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“Benchmark dataset for mid-price forecasting of limit order book data with machine learning methods,”
“Data-driven neural architecture learning for financial time-series forecasting,”
Dat Thanh Tran, Juho Kanniainen, Moncef Gabbouj, and Alexandros Iosifidis, · 2019
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“Forecasting jump arrivals in stock prices: new attention-based network architecture using limit order book data,”
Ymir Mäkinen, Juho Kanniainen, Moncef Gabbouj, and Alexandros Iosifidis, · 2019
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“Deep adaptive input normalization for time series forecasting,”
N. Passalis, A. Tefas, J. Kanniainen, M. Gabbouj, and A. Iosifidis, · 2020
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“Data normalization for bilinear structures in high-frequency financial time-series,”
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Adamantios Ntakaris, Martin Magris, Juho Kanniainen, Moncef Gabbouj, and Alexandros Iosifidis, · 2018
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“Deep learning for limit order books,”
Justin A Sirignano, · 2019
Cited alongside, same era.
“Deeplob: Deep convolutional neural networks for limit order books,”
Zihao Zhang, Stefan Zohren, and Stephen Roberts, · 2019
Cited alongside, same era.
Dat Thanh Tran, Juho Kanniainen, Moncef Gabbouj, and Alexandros Iosifidis, · 2020
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“Attention-based neural bag-of-features learning for sequence data,”
Dat Thanh Tran, Nikolaos Passalis, Anastasios Tefas, Moncef Gabbouj, and Alexandros Iosifidis, · 2020
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“Low-rank temporal attention-augmented bilinear network for financial time-series forecasting,”
M. Shabani and A. Iosifidis, · 2020
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