Document Type

Conference Proceeding

Publisher

Scientific Research Publishing

Faculty

Faculty of Health, Engineering and Science

School

School of Computer and Security Science/eAgriculture Research Group

RAS ID

18226

Comments

This article was originally published as: Leng, J. (2014). Modelling and Analysis on Noisy Financial Time Series. Proceedings of International Conference on Signal and Image Processing. (pp. 64-69). Shenzhen, China. Scientific Research Publishing. Original article available here

Abstract

Building the prediction model(s) from the historical time series has attracted many researchers in last few decades. For example, the traders of hedge funds and experts in agriculture are demanding the precise models to make the prediction of the possible trends and cycles. Even though many statistical or machine learning (ML) models have been proposed, however, there are no universal solutions available to resolve such particular prob-lem. In this paper, the powerful forward-backward non-linear filter and wavelet-based denoising method are introduced to remove the high level of noise embedded in financial time series. With the filtered time series, the statistical model known as autoregression is utilized to model the historical times aeries and make the prediction. The proposed models and approaches have been evaluated using the sample time series, and the experimental results have proved that the proposed approaches are able to make the precise prediction very efficiently and effectively.

DOI

10.4236/jcc.2014.22012

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