Structuring Time Series Data to Gain Insight into Agent Behaviour. / Al-Baghdadi, Najim; Wisniewski, Wojciech; Lindsay, David; Lindsay, Sian; Kalnishkan, Yuri; Watkins, Chris.

Proceedings of the 3rd International Workshop on Big Data for Financial News and Data. IEEE, 2020. p. 5480-5490.

Research output: Chapter in Book/Report/Conference proceedingConference contribution

Published

Standard

Structuring Time Series Data to Gain Insight into Agent Behaviour. / Al-Baghdadi, Najim; Wisniewski, Wojciech; Lindsay, David; Lindsay, Sian; Kalnishkan, Yuri; Watkins, Chris.

Proceedings of the 3rd International Workshop on Big Data for Financial News and Data. IEEE, 2020. p. 5480-5490.

Research output: Chapter in Book/Report/Conference proceedingConference contribution

Harvard

Al-Baghdadi, N, Wisniewski, W, Lindsay, D, Lindsay, S, Kalnishkan, Y & Watkins, C 2020, Structuring Time Series Data to Gain Insight into Agent Behaviour. in Proceedings of the 3rd International Workshop on Big Data for Financial News and Data. IEEE, pp. 5480-5490. https://doi.org/10.1109/BigData47090.2019.9006346

APA

Al-Baghdadi, N., Wisniewski, W., Lindsay, D., Lindsay, S., Kalnishkan, Y., & Watkins, C. (2020). Structuring Time Series Data to Gain Insight into Agent Behaviour. In Proceedings of the 3rd International Workshop on Big Data for Financial News and Data (pp. 5480-5490). IEEE. https://doi.org/10.1109/BigData47090.2019.9006346

Vancouver

Al-Baghdadi N, Wisniewski W, Lindsay D, Lindsay S, Kalnishkan Y, Watkins C. Structuring Time Series Data to Gain Insight into Agent Behaviour. In Proceedings of the 3rd International Workshop on Big Data for Financial News and Data. IEEE. 2020. p. 5480-5490 https://doi.org/10.1109/BigData47090.2019.9006346

Author

Al-Baghdadi, Najim ; Wisniewski, Wojciech ; Lindsay, David ; Lindsay, Sian ; Kalnishkan, Yuri ; Watkins, Chris. / Structuring Time Series Data to Gain Insight into Agent Behaviour. Proceedings of the 3rd International Workshop on Big Data for Financial News and Data. IEEE, 2020. pp. 5480-5490

BibTeX

@inproceedings{3ad227a28f59477dac0ed776d8c9a377,
title = "Structuring Time Series Data to Gain Insight into Agent Behaviour",
abstract = "Here we introduce a data staging algorithm designed to reconstruct multiple time series databases into a partitioned and regularised database. The Data Aggregation Partition Reduction Algorithm, or DAPRA for short, was designed to solve the practical issue of effective and meaningful visualisation of irregularly sampled time series data. This paper firstly discusses the rationale for DAPRA, walking through its design and introduces the theoretical foundation of any DAPRA application. Later we report empirical evidence that demonstrates the practical relevance of DAPRA by its application with large and complex time series datasets from two distinct domains (financial and travel).",
author = "Najim Al-Baghdadi and Wojciech Wisniewski and David Lindsay and Sian Lindsay and Yuri Kalnishkan and Chris Watkins",
year = "2020",
month = feb,
day = "24",
doi = "10.1109/BigData47090.2019.9006346",
language = "English",
isbn = "978-1-7281-0859-9",
pages = "5480--5490",
booktitle = "Proceedings of the 3rd International Workshop on Big Data for Financial News and Data",
publisher = "IEEE",

}

RIS

TY - GEN

T1 - Structuring Time Series Data to Gain Insight into Agent Behaviour

AU - Al-Baghdadi, Najim

AU - Wisniewski, Wojciech

AU - Lindsay, David

AU - Lindsay, Sian

AU - Kalnishkan, Yuri

AU - Watkins, Chris

PY - 2020/2/24

Y1 - 2020/2/24

N2 - Here we introduce a data staging algorithm designed to reconstruct multiple time series databases into a partitioned and regularised database. The Data Aggregation Partition Reduction Algorithm, or DAPRA for short, was designed to solve the practical issue of effective and meaningful visualisation of irregularly sampled time series data. This paper firstly discusses the rationale for DAPRA, walking through its design and introduces the theoretical foundation of any DAPRA application. Later we report empirical evidence that demonstrates the practical relevance of DAPRA by its application with large and complex time series datasets from two distinct domains (financial and travel).

AB - Here we introduce a data staging algorithm designed to reconstruct multiple time series databases into a partitioned and regularised database. The Data Aggregation Partition Reduction Algorithm, or DAPRA for short, was designed to solve the practical issue of effective and meaningful visualisation of irregularly sampled time series data. This paper firstly discusses the rationale for DAPRA, walking through its design and introduces the theoretical foundation of any DAPRA application. Later we report empirical evidence that demonstrates the practical relevance of DAPRA by its application with large and complex time series datasets from two distinct domains (financial and travel).

U2 - 10.1109/BigData47090.2019.9006346

DO - 10.1109/BigData47090.2019.9006346

M3 - Conference contribution

SN - 978-1-7281-0859-9

SP - 5480

EP - 5490

BT - Proceedings of the 3rd International Workshop on Big Data for Financial News and Data

PB - IEEE

ER -