Local Polynomial Coefficient AR Prediction Model for Chaotic Time Series
نویسندگان
چکیده
منابع مشابه
Local averaging optimization for chaotic time series prediction
Local models have emerged as one of the most accurate methods of time series prediction, but their performance is sensitive to the choice of user-specified parameters such as the size of the neighborhood, the embedding dimension, and the distance metric. This paper describes a new method of optimizing these parameters to minimize the multi-step cross-validation error. Empirical results indicate...
متن کاملchaotic time series prediction by auto fuzzy regression model
since the pioneering work of zadeh, fuzzy set theory has been applied to amyriad of areas. song and chissom introduced the concept of fuzzy time series andapplied some methods to the enrolments of the university of alabama. thereafter weapply fuzzy techniques for system identification and apply statistical techniques tomodelling system. an automatic methodology framework that combines fuzzytech...
متن کاملLocal model optimization for time series prediction
Local models have emerged as one of the leading methods of chaotic time series prediction. However, the accuracy of local models is sensitive to the choice of user-speci ed parameters, not unlike neural networks and other methods. This paper describes a method of optimizing these parameters so as to minimize the leave-one-out cross-validation error. This approach reduces the burden on the user ...
متن کاملChaotic Time Series Prediction by Fusing Local Methods
Yong Wang, Shiqiang Hu* School of Aeronautics and Astronautics Shanghai Jiao Tong University, Shanghai [email protected], [email protected] Abstract—In this paper, a novel prediction algorithm is proposed to predict chaotic time series. The chaotic time series can be embedded into state space by Takens embedding theorem. The one dimensional data is mapped to a higher dimensional space that pr...
متن کاملEPNet for Chaotic Time-Series Prediction
EPNet is an evolutionary system for automatic design of arti-cial neural networks (ANNs) 1, 2, 3]. Unlike most previous methods on evolving ANNs, EPNet puts its emphasis on evolving ANN's behaviours rather than circuitry. The parsimony of evolved ANNs is encouraged by the sequential application of architectural mutations. In this paper, EP-Net is applied to a couple of chaotic time-series predi...
متن کاملذخیره در منابع من
با ذخیره ی این منبع در منابع من، دسترسی به آن را برای استفاده های بعدی آسان تر کنید
ژورنال
عنوان ژورنال: Statistical and Application
سال: 2015
ISSN: 2325-2251,2325-226X
DOI: 10.12677/sa.2015.42008