Author of the publication

copy delete add this publication to your clipboard
community post
history of this post
URL
DOI
BibTeX
EndNote
APA
Chicago
DIN 1505
Harvard
MSOffice XML

Benchmark Dataset for Mid-Price Forecasting of Limit Order Book Data with Machine Learning Methods

A. Ntakaris, M. Magris, J. Kanniainen, M. Gabbouj, and A. Iosifidis. (2017)cite arxiv:1705.03233Comment: Published: Journal of Forecasting.
DOI: 10.1002/for.2543

Please choose a person to relate this publication to

To differ between persons with the same name, the academic degree and the title of an important publication will be displayed. You can also use the button next to the name to display some publications already assigned to the person.

Juho Palkeinen

Juho Kannala

Juho Hirvonen

Juho Kim

Juho Mauri Angervo

Other publications of authors with the same name

Temporal Logistic Neural Bag-of-Features for Financial Time series Forecasting leveraging Limit Order Book Data.N. Passalis, A. Tefas, J. Kanniainen, M. Gabbouj, and A. Iosifidis. CoRR, (2019)Deep Adaptive Input Normalization for Price Forecasting using Limit Order Book Data.N. Passalis, A. Tefas, J. Kanniainen, M. Gabbouj, and A. Iosifidis. CoRR, (2019)Augmented Bilinear Network for Incremental Multi-Stock Time-Series Classification.M. Shabani, D. Tran, J. Kanniainen, and A. Iosifidis. CoRR, (2022)Using Deep Learning for price prediction by exploiting stationary limit order book features.A. Tsantekidis, N. Passalis, A. Tefas, J. Kanniainen, M. Gabbouj, and A. Iosifidis. Appl. Soft Comput., (2020)Detachment Problem - Application in Prevention of Information Leakage in Stock Markets.H. Hansen, and J. Kanniainen. CoRR, (2024)Adaptive Normalization for Forecasting Limit Order Book Data Using Convolutional Neural Networks.N. Passalis, A. Tefas, J. Kanniainen, M. Gabbouj, and A. Iosifidis. ICASSP, page 1713-1717. IEEE, (2020)Using Deep Learning for price prediction by exploiting stationary limit order book features.A. Tsantekidis, N. Passalis, A. Tefas, J. Kanniainen, M. Gabbouj, and A. Iosifidis. CoRR, (2018)Temporal Attention-Augmented Bilinear Network for Financial Time-Series Data Analysis.D. Tran, A. Iosifidis, J. Kanniainen, and M. Gabbouj. IEEE Trans. Neural Networks Learn. Syst., 30 (5): 1407-1418 (2019)Matrix-based numerical modelling of financial differential equations.R. Piché, and J. Kanniainen. Int. J. Math. Model. Numer. Optimisation, 1 (1/2): 88-100 (2009)Temporal logistic neural Bag-of-Features for financial time series forecasting leveraging limit order book data.N. Passalis, A. Tefas, J. Kanniainen, M. Gabbouj, and A. Iosifidis. Pattern Recognit. Lett., (2020)

BibSonomy

Disambiguation of "Kanniainen, Juho"

copy delete add this publication to your clipboard
community post
history of this post
URL
DOI
BibTeX
EndNote
APA
Chicago
DIN 1505
Harvard
MSOffice XML

Benchmark Dataset for Mid-Price Forecasting of Limit Order Book Data with Machine Learning Methods

Please choose a person to relate this publication to

Juho Palkeinen

Juho Kannala

Juho Hirvonen

Juho Kim

Juho Mauri Angervo

Other publications of authors with the same name

Disambiguation

BibSonomy

Disambiguation of "Kanniainen, Juho"

copydeleteadd this publication to your clipboardcommunity posthistory of this postURLDOIBibTeXEndNoteAPAChicagoDIN 1505HarvardMSOffice XML Benchmark Dataset for Mid-Price Forecasting of Limit Order Book Data with Machine Learning Methods

Please choose a person to relate this publication to

Juho Palkeinen

Juho Kannala

Juho Hirvonen

Juho Kim

Juho Mauri Angervo

Other publications of authors with the same name

Disambiguation

copy delete add this publication to your clipboard
community post
history of this post
URL
DOI
BibTeX
EndNote
APA
Chicago
DIN 1505
Harvard
MSOffice XML

Benchmark Dataset for Mid-Price Forecasting of Limit Order Book Data with Machine Learning Methods