Penalized least squares for smoothing financial time series

Adrian Letchford, Junbin Gao, Lihong Zheng

Research output: Book chapter/Published conference paperConference paperpeer-review

3 Citations (Scopus)
119 Downloads (Pure)


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.

Original languageEnglish
Title of host publicationAI 2011
Subtitle of host publicationAdvances in Artificial Intelligence - 24th Australasian Joint Conference, Proceedings
Place of PublicationGermany
Number of pages10
ISBN (Print)9783642258312
Publication statusPublished - 2011
EventAustralian Joint Conference on Artificial Intelligence - Perth, Australia
Duration: 05 Dec 201108 Dec 2011

Publication series

NameLecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)
Volume7106 LNAI
ISSN (Print)0302-9743
ISSN (Electronic)1611-3349


ConferenceAustralian Joint Conference on Artificial Intelligence


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