Outlier detection method for time-series data

The invention discloses an outlier detection method for time-series data. The method comprises the following steps of: dividing the time-series data in a training data set by the day from the Monday to the Sunday, and then clustering; establishing a data distribution model, under week granularity, of the time-series data by using the maximum cluster in each clustering result; according to the data distribution model, searching all abnormal values in the training data set, and respectively acquiring the data distribution model at each time interval; by searching, judging whether a periodic event which occurs with time granularity, greater than the week granularity, as a period exists in the abnormal values which accord with the data distribution model at each time interval; if the periodic event exists, recording the periodic event as a class of special period mode; judging whether the time-series data in a test data set accords with a week mode, if so, determining that the time-series data is a non-outlier, otherwise, judging whether the time-series data accords with the special period mode; and if the time-series data accords with the special period mode, determining that the time-series data is the non-outlier, otherwise, determining that the time-series data is an outlier.