Standard
Causal Analysis of Generic Time Series Data Applied for Market Prediction. / Kolonin, Anton; Raheman, Ali; Vishwas, Mukul et al.
Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics). Springer Science and Business Media Deutschland GmbH, 2023. p. 30-39 4 (Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics); Vol. 13539 LNAI).
Research output: Chapter in Book/Report/Conference proceeding › Chapter › Research › peer-review
Harvard
Kolonin, A, Raheman, A, Vishwas, M, Ansari, I, Pinzon, J & Ho, A 2023,
Causal Analysis of Generic Time Series Data Applied for Market Prediction. in
Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)., 4, Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics), vol. 13539 LNAI, Springer Science and Business Media Deutschland GmbH, pp. 30-39.
https://doi.org/10.1007/978-3-031-19907-3_4
APA
Kolonin, A., Raheman, A., Vishwas, M., Ansari, I., Pinzon, J., & Ho, A. (2023).
Causal Analysis of Generic Time Series Data Applied for Market Prediction. In
Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics) (pp. 30-39). [4] (Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics); Vol. 13539 LNAI). Springer Science and Business Media Deutschland GmbH.
https://doi.org/10.1007/978-3-031-19907-3_4
Vancouver
Kolonin A, Raheman A, Vishwas M, Ansari I, Pinzon J, Ho A.
Causal Analysis of Generic Time Series Data Applied for Market Prediction. In Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics). Springer Science and Business Media Deutschland GmbH. 2023. p. 30-39. 4. (Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)). doi: 10.1007/978-3-031-19907-3_4
Author
Kolonin, Anton ; Raheman, Ali ; Vishwas, Mukul et al. /
Causal Analysis of Generic Time Series Data Applied for Market Prediction. Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics). Springer Science and Business Media Deutschland GmbH, 2023. pp. 30-39 (Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)).
BibTeX
@inbook{3bbb5057d1094ef6bdb4e32e1b78cd8b,
title = "Causal Analysis of Generic Time Series Data Applied for Market Prediction",
abstract = "We explore the applicability of the causal analysis based on temporally shifted (lagged) Pearson correlation applied to diverse time series of different natures in context of the problem of financial market prediction. Theoretical discussion is followed by description of the practical approach for specific environment of time series data with diverse nature and sparsity, as applied for environments of financial markets. The data involves various financial metrics computable from raw market data such as real-time trades and snapshots of the limit order book as well as metrics determined upon social media news streams such as sentiment and different cognitive distortions. The approach is backed up with presentation of algorithmic framework for data acquisition and analysis, concluded with experimental results, and summary pointing out at the possibility to discriminate causal connections between different sorts of real field market data with further discussion on present issues and possible directions of the following work.",
keywords = "Causal analysis, Causality, Correlation, Financial market, Time series",
author = "Anton Kolonin and Ali Raheman and Mukul Vishwas and Ikram Ansari and Juan Pinzon and Alice Ho",
year = "2023",
doi = "10.1007/978-3-031-19907-3_4",
language = "English",
isbn = "9783031199066",
series = "Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)",
publisher = "Springer Science and Business Media Deutschland GmbH",
pages = "30--39",
booktitle = "Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)",
address = "Germany",
}
RIS
TY - CHAP
T1 - Causal Analysis of Generic Time Series Data Applied for Market Prediction
AU - Kolonin, Anton
AU - Raheman, Ali
AU - Vishwas, Mukul
AU - Ansari, Ikram
AU - Pinzon, Juan
AU - Ho, Alice
PY - 2023
Y1 - 2023
N2 - We explore the applicability of the causal analysis based on temporally shifted (lagged) Pearson correlation applied to diverse time series of different natures in context of the problem of financial market prediction. Theoretical discussion is followed by description of the practical approach for specific environment of time series data with diverse nature and sparsity, as applied for environments of financial markets. The data involves various financial metrics computable from raw market data such as real-time trades and snapshots of the limit order book as well as metrics determined upon social media news streams such as sentiment and different cognitive distortions. The approach is backed up with presentation of algorithmic framework for data acquisition and analysis, concluded with experimental results, and summary pointing out at the possibility to discriminate causal connections between different sorts of real field market data with further discussion on present issues and possible directions of the following work.
AB - We explore the applicability of the causal analysis based on temporally shifted (lagged) Pearson correlation applied to diverse time series of different natures in context of the problem of financial market prediction. Theoretical discussion is followed by description of the practical approach for specific environment of time series data with diverse nature and sparsity, as applied for environments of financial markets. The data involves various financial metrics computable from raw market data such as real-time trades and snapshots of the limit order book as well as metrics determined upon social media news streams such as sentiment and different cognitive distortions. The approach is backed up with presentation of algorithmic framework for data acquisition and analysis, concluded with experimental results, and summary pointing out at the possibility to discriminate causal connections between different sorts of real field market data with further discussion on present issues and possible directions of the following work.
KW - Causal analysis
KW - Causality
KW - Correlation
KW - Financial market
KW - Time series
UR - https://www.scopus.com/record/display.uri?eid=2-s2.0-85148686602&origin=inward&txGid=7896f6ce9520e6c6f01839b9ae0030b5
UR - https://www.mendeley.com/catalogue/ad9b3851-3f19-3b52-ac0a-bd5dc52c5e7c/
U2 - 10.1007/978-3-031-19907-3_4
DO - 10.1007/978-3-031-19907-3_4
M3 - Chapter
SN - 9783031199066
T3 - Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)
SP - 30
EP - 39
BT - Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)
PB - Springer Science and Business Media Deutschland GmbH
ER -
