1. A computer-implemented method for identifying anomalies in time series data, comprising: at a computer server having one or more processors and memory for storing programs to be executed by the one or more processors: storing in a database time series data for a data source, wherein the time series data comprises a plurality of time-value pairs, each pair including a value of one or more attributes associated with the data source and a time associated with the value; for a particular attribute, generating a plurality of forecasting models for characterizing the time-value pairs in a respective subset of the time series data, each forecasting model including an estimated attribute value and an associated error-variance; for a respective time-value pair associated with the particular attribute: determining a plurality of differences between the value of the time-value pair and respective estimated attribute values of the plurality of forecasting models; tagging the time-value pair as an anomaly if the differences for at least a first subset of the forecasting models are greater than the corresponding error variances; and determining a significance factor such that the respective differences for at least a second subset of the forecasting models are smaller than the corresponding error-variances multiplied by the significance factor, wherein the first subset is within the second subset; and in response to a request from a client application for analytics information for the data source, the request including a predefined significance threshold for one or more of the attributes, reporting to the client application at least a subset of the time-value pairs tagged as anomalies for one or more of the attributes if their respective significance factors exceed the predefined significance threshold.