Time series data clusters are being researched thoroughly. The distance metric drives the development of the clustering time series. The ARIMA model is one of the models that can be employed in model-based clustering, although differing model selection criteria can lead to uncertainty in the model. In this investigation, we created a technique for ensemble distance-based time series data clustering. To express the distance between two series, five distances based on the five model selection criteria are utilized. The average of the five distances reflects the distance of two time series data. According to the simulation results, the ensemble distance method could boost clustering accuracy by more than 11%. Based on the pattern of rainfall levels, we applied our methods to find clusters of locations in the Province of West Java (Indonesia). The findings indicate that the rainfall pattern in the same cluster is similar. The cluster model is effective and feasible for representing individual models in a cluster.