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

Nikolaos Passalis; Anastasios Tefas; Juho Kanniainen; Moncef Gabbouj; Alexandros Iosifidis

doi:10.1109/ICASSP.2019.8682297

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

Nikolaos Passalis, Anastasios Tefas, Juho Kanniainen, Moncef Gabbouj, Alexandros Iosifidis

Computing Sciences

Research output: Chapter in Book/Report/Conference proceeding › Conference contribution › Scientific › peer-review

7 Citations (Scopus)

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.

Original language	English
Title of host publication	2019 IEEE International Conference on Acoustics, Speech, and Signal Processing, ICASSP 2019 - Proceedings
Publisher	IEEE
Pages	7545-7549
Number of pages	5
ISBN (Electronic)	9781479981311
DOIs	https://doi.org/10.1109/ICASSP.2019.8682297
Publication status	Published - 1 May 2019
Publication type	A4 Article in conference proceedings
Event	IEEE International Conference on Acoustics, Speech, and Signal Processing - Brighton, United Kingdom Duration: 12 May 2019 → 17 May 2019

Conference

Conference	IEEE International Conference on Acoustics, Speech, and Signal Processing
Country/Territory	United Kingdom
City	Brighton
Period	12/05/19 → 17/05/19

Keywords

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

Publication forum classification

Publication forum level 1

ASJC Scopus subject areas

Software
Signal Processing
Electrical and Electronic Engineering

Access to Document

10.1109/ICASSP.2019.8682297

Cite this

@inproceedings{f7e36ea81af74b478e05cae5e40dc75e,

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

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",

author = "Nikolaos Passalis and Anastasios Tefas and Juho Kanniainen and Moncef Gabbouj and Alexandros Iosifidis",

note = "EXT={"}Tefas, Anastasios{"} EXT={"}Iosifidis, Alexandros{"}; IEEE International Conference on Acoustics, Speech, and Signal Processing ; Conference date: 12-05-2019 Through 17-05-2019",

year = "2019",

month = may,

day = "1",

doi = "10.1109/ICASSP.2019.8682297",

language = "English",

pages = "7545--7549",

booktitle = "2019 IEEE International Conference on Acoustics, Speech, and Signal Processing, ICASSP 2019 - Proceedings",

publisher = "IEEE",

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}

Passalis, N, Tefas, A, Kanniainen, J , Gabbouj, M & Iosifidis, A 2019, Deep Temporal Logistic Bag-of-features for Forecasting High Frequency Limit Order Book Time Series. in 2019 IEEE International Conference on Acoustics, Speech, and Signal Processing, ICASSP 2019 - Proceedings. IEEE, pp. 7545-7549, IEEE International Conference on Acoustics, Speech, and Signal Processing, Brighton, United Kingdom, 12/05/19. https://doi.org/10.1109/ICASSP.2019.8682297

Deep Temporal Logistic Bag-of-features for Forecasting High Frequency Limit Order Book Time Series. / Passalis, Nikolaos; Tefas, Anastasios; Kanniainen, Juho et al.
2019 IEEE International Conference on Acoustics, Speech, and Signal Processing, ICASSP 2019 - Proceedings. IEEE, 2019. p. 7545-7549.

Research output: Chapter in Book/Report/Conference proceeding › Conference contribution › Scientific › peer-review

TY - GEN

T1 - Deep Temporal Logistic Bag-of-features for Forecasting High Frequency Limit Order Book Time Series

AU - Passalis, Nikolaos

AU - Tefas, Anastasios

AU - Kanniainen, Juho

AU - Gabbouj, Moncef

AU - Iosifidis, Alexandros

N1 - EXT="Tefas, Anastasios" EXT="Iosifidis, Alexandros"

PY - 2019/5/1

Y1 - 2019/5/1

N2 - 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.

AB - 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.

KW - Limit Order Book

KW - Temporal Bag-of-Features

KW - Time series forecasting

U2 - 10.1109/ICASSP.2019.8682297

DO - 10.1109/ICASSP.2019.8682297

M3 - Conference contribution

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BT - 2019 IEEE International Conference on Acoustics, Speech, and Signal Processing, ICASSP 2019 - Proceedings

PB - IEEE

T2 - IEEE International Conference on Acoustics, Speech, and Signal Processing

Y2 - 12 May 2019 through 17 May 2019

ER -

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

Abstract

Conference

Keywords

Publication forum classification

ASJC Scopus subject areas

Access to Document

Fingerprint

Cite this