Abstract: An unsupervised density-ratio estimation (DRE) based approach is used to determine statistical changes in time-series data when no knowledge of the pre- and post-change distributions are available. The core idea behind the disclosed technology is to split the time-series at an arbitrary point and estimate the ratio of densities of distribution (using a parametric model such as a neural network) before and after the split point. The DRE-CUSUM change detection statistic is then derived from the cumulative sum (CUSUM) of the logarithm of the estimated density ratio. Theoretical justification as well as accuracy guarantees are provided which show that the proposed statistic can reliably detect statistical changes, irrespective of the split point. The disclosed framework makes it readily applicable in various practical settings (including high-dimensional time-series data).