eprintid: 3325
rev_number: 2
eprint_status: archive
userid: 1
dir: disk0/00/00/33/25
datestamp: 2023-11-09 15:51:35
lastmod: 2023-11-09 15:51:35
status_changed: 2023-11-09 15:46:34
type: article
metadata_visibility: show
creators_name: Jadid Abdulkadir, S.
creators_name: Yong, S.-P.
title: Unscented Kalman filter for noisy multivariate financial time-series data
ispublished: pub
keywords: multivariate; noise; Sigma point; Statistical estimation; Stock price movements; Unscented Kalman Filter; Unscented Kalman filtering; Unscented transformations, Artificial intelligence; Commerce; Financial data processing; Kalman filters; Nonlinear filtering, Estimation
note: cited By 18; Conference of 7th Multi-Disciplinary International Workshop on Artificial Intelligence, MIWAI 2013 ; Conference Date: 9 December 2013 Through 11 December 2013; Conference Code:101862
abstract: Kalman filter is one of the novel techniques useful for statistical estimation theory and now widely used in many practical applications. In this paper, we consider the process of applying Unscented Kalman Filtering algorithm to multivariate financial time series data to determine if the algorithm could be used to smooth the direction of KLCI stock price movements using five different measurement variance values. Financial data are characterized by non-linearity, noise, chaotic in nature, volatile and the biggest impediment is due to the colossal nature of the capacity of transmitted data from the trading market. Unscented Kalman filter employs the use of unscented transformation commonly referred to as sigma points from which estimates are recovered from. The filtered output precisely internments the covariance of noisy input data producing smoothed and less noisy estimates. © 2013 Springer-Verlag.
date: 2013
official_url: https://www.scopus.com/inward/record.uri?eid=2-s2.0-84892425765&doi=10.1007%2f978-3-642-44949-9_9&partnerID=40&md5=43ff824734590a9478a4b9878861ec82
id_number: 10.1007/978-3-642-44949-9₉
full_text_status: none
publication: Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)
volume: 8271 L
place_of_pub: Krabi
pagerange: 87-96
refereed: TRUE
isbn: 9783642449482
issn: 03029743
citation:   Jadid Abdulkadir, S. and Yong, S.-P.  (2013) Unscented Kalman filter for noisy multivariate financial time-series data.  Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics), 8271 L.  pp. 87-96.  ISSN 03029743