Skip Nav Destination
ASME Press Select Proceedings
International Conference on Computer Technology and Development, 3rd (ICCTD 2011)
ISBN:
9780791859919
No. of Pages:
2000
Publisher:
ASME Press
Publication date:
2011
eBook Chapter
330 Research and Application of Piecewise Linear Fitting Algorithm Based on Stock Time Series
By
Weihong Wang
Dept. of Computer Science, Zhejiang University of Technology , Hangzhou ,; wwh@zjut.edu.cn
,
Weihong Wang
Search for other works by this author on:
Xiangbin Zheng
Dept. of Computer Science, Zhejiang University of Technology , Hangzhou ,; jyjyioi@163.com
,
Xiangbin Zheng
Search for other works by this author on:
Song Wang
Dept. of Computer Science, Zhejiang University of Technology , Hangzhou ,; jyjyioi@163.com
,
Song Wang
Search for other works by this author on:
Zhaolin Fang
Dept. of Computer Science, Zhejiang University of Technology , Hangzhou ,; jyjyioi@163.com
,
Zhaolin Fang
Search for other works by this author on:
Chunping Wang
Dept. of Computer Science, Zhejiang University of Technology , Hangzhou ,; jyjyioi@163.com
Chunping Wang
Search for other works by this author on:
Page Count:
5
-
Published:2011
Citation
Wang, W, Zheng, X, Wang, S, Fang, Z, & Wang, C. "Research and Application of Piecewise Linear Fitting Algorithm Based on Stock Time Series." International Conference on Computer Technology and Development, 3rd (ICCTD 2011). Ed. Zhou, J. ASME Press, 2011.
Download citation file:
This paper discusses an Improved Linear Fitting Algorithm Based on Stock Time Series (SPLR) .Firstly, the algorithm defines a set of stock trend points and traversals the stock's time series, and finds the stock trend points by extremes method and the investors' experience threshold value. Then it finds the important trend point by the difference of the slope of triangle edge. Finally, connecting these trend points which are found by the above method to present piecewise linear of the stock's time series. This paper's experiment compares with several other fitting algorithms and computes the errors, and the results show: this...
Abstract
Key Words
1 Introduction
2 The Basic Definitions
3 Algorithm Descriptions
4 Summaries and Prospect
Acknowledgment
References
This content is only available via PDF.
You do not currently have access to this chapter.
Email alerts
Related Chapters
Prediction of Coal Mine Gas Concentration Based on Constructive Neural Network
International Conference on Information Technology and Computer Science, 3rd (ITCS 2011)
Fitting a Function and Its Derivative
Intelligent Engineering Systems Through Artificial Neural Networks, Volume 17
Covariance Regularization for Supervised Learning in High Dimensions
Intelligent Engineering Systems through Artificial Neural Networks, Volume 20
Conclusions and Outlook
Modified Detrended Fluctuation Analysis (mDFA)
Related Articles
A Comparison of Long-Term Wind Speed Forecasting Models
J. Sol. Energy Eng (November,2010)
Throughput Bottleneck Prediction of Manufacturing Systems Using Time Series Analysis
J. Manuf. Sci. Eng (April,2011)
Detecting and Predicting Early Faults of Complex Rotating Machinery Based on Cyclostationary Time Series Model
J. Vib. Acoust (October,2006)