Name
Abstract | ||
|---|---|---|
Lots of researchers have paid attention to time series clustering in recent years. This paper studies the stationarity analysis for autoregressive and moving average models of time series with clustering, firstly presents a set of nonlinear functions, or rather the square function along with logarithmic function to better autocorrelation function, secondly clusters time series into stationary and non-stationary with Clustering, finally an automatic mechanism for prejudging the stationarity of time series is presented. The proposed approach has been tested using two datasets, one natural and one synthetic, and is shown to yield useful and robust result of stationarity analysis. |
Year | DOI | Venue |
|---|---|---|
2010 | 10.1109/FSKD.2010.5569228 | FSKD |
Keywords | Field | DocType |
square function,pattern clustering,moving average processes,clustering univariate time series,autocorrelation function,nonlinear function,stationarity,moving average model,automatic mechanism,logarithmic function,nonlinear functions,nonlinear,stationarity analysis,time series,economics,time measurement,correlation,predictive models,time series analysis,robustness,moving average | Order of integration,Time series,Applied mathematics,Artificial intelligence,Cluster analysis,Moving-average model,Autocorrelation,Autoregressive model,Mathematical optimization,Correlation clustering,Pattern recognition,Moving average,Mathematics | Conference |
Volume | ISBN | Citations |
6 | 978-1-4244-5931-5 | 0 |
PageRank | References | Authors |
0.34 | 2 | 4 |
Name | Order | Citations | PageRank |
|---|---|---|---|
| Heshan Guan | 1 | 5 | 2.31 |
| Shuliang Zou | 2 | 2 | 2.08 |
| Mengya Liu | 3 | 15 | 1.20 |
| Tieli Wang | 4 | 0 | 0.34 |