In　this　paper,　we　propose　two　anomaly　detection　algorithms　PAV　and　MPAV　on　time　series.　The　first　basic　idea　of　this　paper　defines　that　the　anomaly　pattern　is　the　most　infrequent　time　series　pattern,　which　is　the　lowest　support　pattern.　The　second　basic　idea　of　this　paper　is　that　PAV　detects　directly　anomalies　in　the　original　time　series,　and　MPAV　algorithm　extraction　anomaly　in　the　wavelet　approximation　coefficient　of　the　time　series.　For　complexity　analyses,　as　the　wavelet　transform　have　the　functions　to　compress　data,　filter　noise,　and　maintain　the　basic　form　of　time　series,　the　MPAV　algorithm,　while　maintaining　the　accuracy　of　the　algorithm　improves　the　efficiency.　As　PAV　and　MPAV　algorithms　are　simple　and　easy　to　realize　without　training,　this　proposed　multi-scale　anomaly　detection　algorithm　based　on　infrequent　pattern　of　time　series　can　therefore　be　proved　to　be　very　useful　for　computer　science　applications.　©　2007　Elsevier　B.V.　All　rights　reserved.