Managing the prediction of metrics in high-frequency financial markets is a
challenging task. An efficient way is by monitoring the dynamics of a limit
order book to identify the information edge. This paper describes the first
publicly available benchmark dataset of high-frequency limit order markets for
mid-price prediction. We extracted normalized data representations of time
series data for five stocks from the NASDAQ Nordic stock market for a time
period of ten consecutive days, leading to a dataset of ~4,000,000 time series
samples in total. A day-based anchored cross-validation experimental protocol
is also provided that can be used as a benchmark for comparing the performance
of state-of-the-art methodologies. Performance of baseline approaches are also
provided to facilitate experimental comparisons. We expect that such a
large-scale dataset can serve as a testbed for devising novel solutions of
expert systems for high-frequency limit order book data analysis.
Description
Benchmark Dataset for Mid-Price Forecasting of Limit Order Book Data with Machine Learning Methods
%0 Generic
%1 ntakaris2017benchmark
%A Ntakaris, Adamantios
%A Magris, Martin
%A Kanniainen, Juho
%A Gabbouj, Moncef
%A Iosifidis, Alexandros
%D 2017
%K LOB finance time_series
%R 10.1002/for.2543
%T Benchmark Dataset for Mid-Price Forecasting of Limit Order Book Data
with Machine Learning Methods
%U http://arxiv.org/abs/1705.03233
%X Managing the prediction of metrics in high-frequency financial markets is a
challenging task. An efficient way is by monitoring the dynamics of a limit
order book to identify the information edge. This paper describes the first
publicly available benchmark dataset of high-frequency limit order markets for
mid-price prediction. We extracted normalized data representations of time
series data for five stocks from the NASDAQ Nordic stock market for a time
period of ten consecutive days, leading to a dataset of ~4,000,000 time series
samples in total. A day-based anchored cross-validation experimental protocol
is also provided that can be used as a benchmark for comparing the performance
of state-of-the-art methodologies. Performance of baseline approaches are also
provided to facilitate experimental comparisons. We expect that such a
large-scale dataset can serve as a testbed for devising novel solutions of
expert systems for high-frequency limit order book data analysis.
@misc{ntakaris2017benchmark,
abstract = {Managing the prediction of metrics in high-frequency financial markets is a
challenging task. An efficient way is by monitoring the dynamics of a limit
order book to identify the information edge. This paper describes the first
publicly available benchmark dataset of high-frequency limit order markets for
mid-price prediction. We extracted normalized data representations of time
series data for five stocks from the NASDAQ Nordic stock market for a time
period of ten consecutive days, leading to a dataset of ~4,000,000 time series
samples in total. A day-based anchored cross-validation experimental protocol
is also provided that can be used as a benchmark for comparing the performance
of state-of-the-art methodologies. Performance of baseline approaches are also
provided to facilitate experimental comparisons. We expect that such a
large-scale dataset can serve as a testbed for devising novel solutions of
expert systems for high-frequency limit order book data analysis.},
added-at = {2023-04-12T21:14:52.000+0200},
author = {Ntakaris, Adamantios and Magris, Martin and Kanniainen, Juho and Gabbouj, Moncef and Iosifidis, Alexandros},
biburl = {https://www.bibsonomy.org/bibtex/21084e237d3eae2bcd13234670a850a2b/qilinw},
description = {Benchmark Dataset for Mid-Price Forecasting of Limit Order Book Data with Machine Learning Methods},
doi = {10.1002/for.2543},
interhash = {161c79160a357fb09304269fe1af645e},
intrahash = {1084e237d3eae2bcd13234670a850a2b},
keywords = {LOB finance time_series},
note = {cite arxiv:1705.03233Comment: Published: Journal of Forecasting},
timestamp = {2023-04-12T21:16:13.000+0200},
title = {Benchmark Dataset for Mid-Price Forecasting of Limit Order Book Data
with Machine Learning Methods},
url = {http://arxiv.org/abs/1705.03233},
year = 2017
}