Abstract: Computer systems and associated methods are disclosed to detect a future change point in time series data used as input to a machine learning model. A forecast for the time series data is generated. In some embodiments, a fitting model is generated from the time series data, and residuals of the fitting model are obtained for respective portions of the data both before and after a potential change point in the future. The change point is determined based on a ratio of residual metrics for the two portions. In some embodiments, data features are extracted from individual segments in the time series data, and the segments are clustered based on their data features. A change point is determined based on a dissimilarity in cluster assignments for segments before and after the point. In some embodiments, when a change point is predicted, an update of the machine learning model is triggered.

Abstract: System and methods are provided that can address a slowdown during neural network execution. The machine learning system precomputes values at the input layer that are not going to change for subsequent inferences. Using the precomputed values during execution reduces the computation costs for determining inferences in neural networks. Some of the improved neural networks are configured to maximize performance of an agent. Further, some of the improved neural networks are configured to process multiple agents where the input layer is configured to receive agent feature vectors in the input layer.