@article{FEI2006, 
author = {Wanchun FEI and Lun BAI},
title = {Pattern Recognition of Non-Stationary Time Series with Finite Length},
year = {2006},
journal = {Tsinghua Science and Technology},
volume = {11},
number = {5},
pages = {611-616},
keywords = {time series analysis, non-stationarity, pattern recognition, size series of cocoon filaments},
url = {https://www.sciopen.com/article/10.1016/S1007-0214(06)70241-3},
doi = {10.1016/S1007-0214(06)70241-3},
abstract = {Statistical learning and recognition methods were used to extract the characteristics of size series measurements of cocoon filaments that are non-stationary in terms of mean and auto-covariance, by using the time varying parameter auto-regressive (TVPAR) model. After the system was taught to recognize the size data, the system correctly recognized the size of series of cocoon filaments as much as 96.95% of the time for a single series and 98.72% of the time for the mean of two series. The correct recognition rate was higher after suitable filtering. The theory and method can be used to analyze other types of non-stationary finite length time series.}
}