Penalized Least Squares for Smoothing Financial Time Series
Modeling of financial time series data by methods of artificial intelligence is difficult because of the extremely noisy nature of the data. A common and simple form of filter to reduce the noise originated in signal processing, the finite impulse response (FIR) filter. There are several of these noise reduction methods used throughout the financial instrument trading community. The major issue with these filters is the delay between the filtered data and the noisy data. This delay only increases as more noise reduction is desired. In the present marketplace, where investors are competing for quality and timely information, this delay can be a hindrance. This paper proposes a new FIR filter derived with the aim of maximizing the level of noise reduction and minimizing the delay. The model is modified from the old problem of time series graduation by penalized least squares. Comparison between five different methods has been done and experiment results have shown that our method is significantly superior to the alternatives in both delay and smoothness over short and middle range delay periods.
KeywordsPenalized least squares Time series analysis Financial analysis Finite impulse response Time series data mining
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- 5.Ehlers, J.F.: Rocket science for traders: Digital signal processing applications. John Wiley & Sons, Inc., New York (2001)Google Scholar
- 11.Hale, D.: Recursive gaussian filters. Tech. rep., Center for Wave Phenomena (2006)Google Scholar
- 12.Hassani, H.: Singular spectrum analysis: Methodology and comparison. Journal of Data Sciences 5, 239–257 (2007), mPRA PaperGoogle Scholar
- 16.Hull, A.: Hull moving average HMA (2011), http://www.justdata.com.au/Journals/AlanHull/hull_ma.htm
- 19.Karunasingha, D.S.K., Liong, S.Y.: Enhancement of chaotic time series prediction with real-time noise reduction. In: International Conference on Small Hydropower - Hydro Sri Lanka (2007)Google Scholar
- 20.Klaas, M., Briers, M., Freitas, N.d., Doucet, A., Maskell, S., Lang, D.: Fast particle smoothing: if I had a million particles. In: Proceedings of the 23rd International Conference on Machine Learning, pp. 481–488 (2006)Google Scholar
- 23.Legoux, A.: ALMA Arnaud Legous Moving Average (2009), http://www.arnaudlegoux.com/wp-content/uploads/2011/03/ALMA-Arnaud-Legoux-Moving-Average.pdf (2011)
- 27.Nikpour, M., Nadernejad, E., Ashtiani, H., Hassanpour, H.: Using pde’s for noise reduction in time series. International Journal of Computing and ICT Research 3(1), 42–48 (2009)Google Scholar
- 33.Whittaker, E.T.: On a new method of graduation. Proceedings of the Edinburgh Mathematical Society 41(-1), 63–75 (1923)Google Scholar
- 34.Zehtabian, A., Hassanpour, H.: A non-destructive approach for noise reduction in time domain. World Applied Sciences Journal 6(1), 53–63 (2009)Google Scholar