Microsoft Technology Licensing, LLC

Ishani Shailesh Parikh

Aditya Bandi

Laurent Visconti

Techniques are provided for ranking time-series including previously detected anomalous fact quantity changes over an associated time interval. Time-series are received, and for each time-series, a normalized fact quantity change is determined, and each time-series is ranked based in part on the normalized fact quantity change. A normalized fact quantity change may be determined by determining a normalization factor over the time interval, and then determining a product of the normalization factor and the absolute value of the fact quantity change of that time interval. Alternatively, a normalized fact quantity change may be the product of the normalization factor, a predetermined order factor, and the absolute value of the fact quantity change. The normalization factor is determined by analyzing the distribution of the fact quantity change over dimension values of the dimension(s) associated with the time-series to determine the number of values in which the fact quantity is concentrated.

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Bellevue, WA, United States of America

Ishani Shailesh Parikh