Splunk Inc.

Amazon Technologies, Inc.

Manwah Wong

Adam Jamison Oliner

Manish Sainani

Aungon Nag Radon

Harsh Keswani

Kai Fan Tang

Christopher Thomas Lewis

Embodiments of the present invention are directed to facilitating event forecasting. In accordance with aspects of the present disclosure, a set of events determined from raw machine data is obtained. The events are analyzed to identify leading indicators that indicate a future occurrence of a target event, wherein the leading indicators occur during a search period of time the precedes a warning period of time, thereby providing time for an action to be performed prior to an occurrence of a predicted target event. At least one of the leading indicators is used to predict a target event. An event notification is provided indicating the prediction of the target event.

Identifying leading indicators for target event prediction

Techniques are described for filtering and normalizing training data used to build a predictive auto scaling model used by a service provider network to proactively scale users' computing resources. Further described are techniques for identifying collections of computing resources that exhibit suitably predictable usage patterns such that a predictive auto scaling model can be used to forecast future usage patterns with reasonable accuracy and to scale the resources based on such generated forecasts. The filtering of training data and the identification of suitably predictable collections of computing resources are based in part on autocorrelation analyses, and in particular on 'delayed' autocorrelation analyses, of time series data, among other techniques described herein.

Using delayed autocorrelation to improve the predictive scaling of computing resources

Event forecasting

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Manwah Wong