NDLI: Filtering financial time series by least squares

Content Provider	Springer Nature Link
Author	Letchford, Adrian Gao, Junbin Zheng, Lihong
Copyright Year	2012
Abstract	Modeling of financial time series with artificial intelligence is difficult due to the random nature of the data. The moving average filter is a common and simple form of filter to reduce this noise. There are several of these noise reduction methods used throughout the financial security trading community. The major issue with these filters is the lag between the filtered data and the noisy data. This lag only increases as more noise reduction is desired. In the present marketplace, where investors are competing for quality and timely information, lag can be a hindrance. This paper proposes a new moving average filter derived with the aim of maximizing the level of noise reduction at each delay. Comparison between five different methods has been done and experiment results have shown that our method is a superior noise reducer to the alternatives over short and middle range lag periods.
Starting Page	149
Ending Page	154
Page Count	6
File Format	PDF
ISSN	18688071
Journal	International Journal of Machine Learning and Cybernetics
Volume Number	4
Issue Number	2
e-ISSN	1868808X
Language	English
Publisher	Springer-Verlag
Publisher Date	2012-02-08
Publisher Place	Berlin, Heidelberg
Access Restriction	One Nation One Subscription (ONOS)
Subject Keyword	Filtering Smoothing Time series analysis Time series data mining Computational Intelligence Artificial Intelligence (incl. Robotics) Control, Robotics, Mechatronics Statistical Physics, Dynamical Systems and Complexity Systems Biology Pattern Recognition
Content Type	Text
Resource Type	Article
Subject	Artificial Intelligence Computer Vision and Pattern Recognition Software

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