Systems And Methods For Forecasting Time Series With Variable Seasonality

Techniques for training and evaluating seasonal forecasting models are disclosed. In some embodiments, a network service generates, in memory, a set of data structures that separate sample values by season type and season space. The set of data structures may include a first set of clusters correspo...

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Bibliographische Detailangaben
Hauptverfasser: Garvey, Dustin, Shaft, Uri, Salunke, Sampanna Shahaji, Gopalakrishnan, Sumathi
Format: Patent
Sprache:eng
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Zusammenfassung:Techniques for training and evaluating seasonal forecasting models are disclosed. In some embodiments, a network service generates, in memory, a set of data structures that separate sample values by season type and season space. The set of data structures may include a first set of clusters corresponding to different season types in the first season space and a second set of clusters corresponding to different season types in the second season space. The network service merges two or more clusters the first set and/or second set of clusters. Clusters from the first set are not merged with clusters from the second set. After merging the clusters, the network serves determines a trend pattern for each of the remaining clusters in the first and second set of clusters. The network service then generates a forecast for a metric of a computing resource based on the trend patterns for each remaining cluster.