This paper explores the performance of clustered neural community aggregators (CNNA) for time series. Through the years, time collection evaluation has allowed for insights into numerous phenomena, ranging from low cost to environmental and organic and clustered neural networks’ purpose to provide higher outcomes via mixing a couple of neural networks. However, their effectiveness has yet to be thoroughly studied in time collection. In this paper, several CNNA architectures are compared against their non-clustered equivalents, and the results are evaluated across an expansion of time collection datasets. Additionally, the overall performance of different clustering algorithms is studied in terms of clustering first-class and time cost. The outcomes show that, compared to non-clustered fashions, CNNA can substantially enhance the prediction performance of time collection and that the effectiveness of various types of clustering algorithms depends substantially on the kind of time collection analyzed.