Abstract: Systems and methods for anomaly detection and guided analysis using structural time-series model. A server may receive a request from a client to analyze a time-series data comprising a plurality of data points. A database of global calendars may be accessed. A structural time-series model may be built from the time-series data and the database of global calendars, the structural time-series model comprising a hidden structure and a plurality of probability distributions, each probability distribution corresponding to a data point. For each data point of the time-series data, a range of expected values is determined from a respective probability distribution, the range of expected values capturing a predefined percentage of the respective probability distribution. An anomaly is detected at a first data point of the time-series data responsive to comparing the first data point with a respective range of expected values. The anomaly is transmitted to the client for display with the time-series data.

Abstract: A method includes: obtaining, at time intervals, subsets of data from a database using code of a query language; performing a pre-aggregation on the subsets of data to produce pre-aggregated data; storing the pre-aggregated data in the database; obtaining, in response to a query, at least some of the pre-aggregated data from the database, where the at least some of the pre-aggregated data is obtained using code from the query language used to obtain the subsets of data; and performing a re-aggregation on the pre-aggregated data to produce re-aggregated data