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Title:Neural Network Learning for Time-Series Predictions Using Constrained Formulations
Author(s):Qian, Minglun
Doctoral Committee Chair(s):Wah, Benjamin W.
Department / Program:Computer Science
Discipline:Computer Science
Degree Granting Institution:University of Illinois at Urbana-Champaign
Degree:Ph.D.
Genre:Dissertation
Subject(s):Computer Science
Abstract:When using a constrained formulation along with violation guided backpropagation to neural network learning for near noiseless time-series benchmarks, we achieve much improved prediction performance as compared to that of previous work, while using less parameters. For noisy time-series, such as financial time series, we have studied systematically trade-offs between denoising and information preservation, and have proposed three preprocessing techniques for time-series with high-frequency noise. In particular, we have proposed a novel approach by first decomposing a noisy time series into different frequency channels and by preprocessing each channel adaptively according to its level of noise. We incorporate constraints on predicting low-pass data in the lag period when a low-pass filter is employed to denoise the band. The new constraints enable active training in the lag period that greatly improves the prediction accuracy in the lag period. Extensive prediction experiments on financial time series have been conducted to exploit the modeling ability of neural networks, and promising results have been obtained.
Issue Date:2005
Type:Text
Language:English
Description:276 p.
Thesis (Ph.D.)--University of Illinois at Urbana-Champaign, 2005.
URI:http://hdl.handle.net/2142/81666
Other Identifier(s):(MiAaPQ)AAI3182358
Date Available in IDEALS:2015-09-25
Date Deposited:2005


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