Model-based clustering algorithm for time-series with irregular interval
Cuiyu Li · Computer Engineering and Applications Journal · 2008
Most existing clustering methods can only work with fixed-interval representations of data,ignoring the variance of time axis.A model-based clustering approach using cepstrum distance metrics and Autoregressive Conditional Duration (ACD) model is proposed,it integrates the merits of parametric econometrics and non-parametric clustering,and is fit for time series with irregular interval.Experimental results show that this method is generally effective in clustering irregular space time series,and conclusion inferred from experiment results is agree with the market microstructure theories.