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

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Yksityiskohdat

AlkuperäiskieliEnglanti
Otsikko2019 IEEE International Conference on Acoustics, Speech, and Signal Processing, ICASSP 2019 - Proceedings
KustantajaIEEE
Sivut7545-7549
Sivumäärä5
ISBN (elektroninen)9781479981311
DOI - pysyväislinkit
TilaJulkaistu - 1 toukokuuta 2019
OKM-julkaisutyyppiA4 Artikkeli konferenssijulkaisussa
TapahtumaIEEE International Conference on Acoustics, Speech, and Signal Processing - Brighton, Iso-Britannia
Kesto: 12 toukokuuta 201917 toukokuuta 2019

Conference

ConferenceIEEE International Conference on Acoustics, Speech, and Signal Processing
MaaIso-Britannia
KaupunkiBrighton
Ajanjakso12/05/1917/05/19

Tiivistelmä

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.

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