Unscented Kalman filter for noisy multivariate financial time-series data

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.

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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.

Item Type: Article
Impact Factor: cited By 16
Uncontrolled 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
Depositing User: Ms Sharifah Fahimah Saiyed Yeop
Date Deposited: 30 Mar 2022 01:10
Last Modified: 30 Mar 2022 01:10
URI: http://scholars.utp.edu.my/id/eprint/32949

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