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Deep Temporal Logistic Bag-of-features for Forecasting High Frequency Limit Order Book Time Series

Research output: Chapter in Book/Report/Conference proceedingConference contributionScientificpeer-review

Details

Original languageEnglish
Title of host publication2019 IEEE International Conference on Acoustics, Speech, and Signal Processing, ICASSP 2019 - Proceedings
PublisherIEEE
Pages7545-7549
Number of pages5
ISBN (Electronic)9781479981311
DOIs
Publication statusPublished - 1 May 2019
Publication typeA4 Article in a conference publication
EventIEEE International Conference on Acoustics, Speech, and Signal Processing - Brighton, United Kingdom
Duration: 12 May 201917 May 2019

Conference

ConferenceIEEE International Conference on Acoustics, Speech, and Signal Processing
CountryUnited Kingdom
CityBrighton
Period12/05/1917/05/19

Abstract

Forecasting time series has several applications in various domains. The vast amount of data that are available nowadays provide the opportunity to use powerful deep learning approaches, but at the same time pose significant challenges of high-dimensionality, velocity and variety. In this paper, a novel logistic formulation of the well-known Bag-of-Features model is proposed to tackle these challenges. The proposed method is combined with deep convolutional feature extractors and is capable of accurately modeling the temporal behavior of time series, forming powerful forecasting models that can be trained in an end-to-end fashion. The proposed method was extensively evaluated using a large-scale financial time series dataset, that consists of more than 4 million limit orders, outperforming other competitive methods.

Keywords

  • Limit Order Book, Temporal Bag-of-Features, Time series forecasting

Publication forum classification

Field of science, Statistics Finland