Abstract: A time series forecasting service system is disclosed. The system identifies a set of cross-validation parameters to be used for cross-validating a model to be used for generating a requested forecast. The requested forecast includes a time series dataset and a forecast horizon identifying a number of time steps for which a forecast is to be made using the time series dataset. The system identifies an objective function to be minimized for determining optimal values for the set of cross-validation parameters and identifies constraints for the cross-validation parameters. The system uses an optimization technique to determine the optimal values for the cross-validation parameters. The optimization technique performs processing that determines the optimal values by minimizing the objective function while satisfying the set of constraints. The system uses the optimal values for the cross-validation parameters to perform cross-validation of the model to be used for making the requested forecast.

Abstract: Techniques for providing request throttling using proportional, integral, and exponential smoothing algorithms are disclosed. A distributed computing system can include a throttler engine that receives a plurality of requests targeting a software component within the distributed computing system. The throttler engine can aggregate the requests into a queue based on a time window. The throttler engine can determine a received request rate and a request rate limit for the software component and then compute a throttled request rate. The throttled request rate can include correction terms derived from proportional and integral computations and a correction term obtained from an exponential smoothing algorithm. The throttler engine can then provide throttled requests from the queue to the software component.