Last Significant Trend Change Detection Method for Offline Poisson Distribution Datasets

Trend change detection methods find trends in a dataset. Datasets based on Poisson distribution are important to analyze since they mimic many different applications such as computer networks. Our use-cases are simulations of computer networks. The last significant trend is the last predominant trend in a time-series dataset. Our method is a matrix based trend change detection that can analyze datasets with variable sizes. Reducing the time complexity and increasing the accuracy when determining the last significant trend are the goals of our method. We compare our method with RuLSIF, a basic change point detection method, to illustrate the benefits of our approach.