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S. M. Pincus and E. K Kalman, “Irregularity, Volatility, Risk, and Financial Market Time Series,” Proceedings of the National Academy of Sciences, Vol. 101, No. 38, 2004, pp. 13709-13714. http://dx.doi.org/10.1073/pnas.0405168101

has been cited by the following article:

• TITLE: Modelling and Analysis on Noisy Financial Time Series

  AUTHORS: Jinsong Leng

  KEYWORDS: Financial Time Series; Filtering and Denoising; Autoregression; Modelling and Prediction

  JOURNAL NAME: Journal of Computer and Communications, Vol.2 No.2, January 10, 2014

  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 problem. 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.