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Abstract: biclustering is a method of grouping objects and attributes simultaneously in order to find multiple hidden patterns. When dealing with a long time series, there is a low possibility of finding meaningful clusters of whole time sequence. However, we may find more significant clusters containing partial time sequence by applying a biclustering method. This paper proposed a new biclustering algorithm for time series data following an autoregressive moving average (ARMA) model. We assumed the plaid model but modified the algorithm to incorporate the sequential nature of time series data. The maximum likelihood estimation (MLE) method was used to estimate coefficients of ARMA in each bicluster. We applied the proposed method to several synthetic data which were generated from different ARMA orders. Results from the experiments showed that the proposed method compares favorably with other biclustering methods for time series data.

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