Reducing　the　negative　influence　of　stochastic　disturbances　in　sample　data　has　always　been　a　difficult　problem　in　time　series　analysis.　In　this　paper,　three　new　fractional　weakening　buffer　operators　are　proposed,　and　then　some　desirable　properties　of　these　proposed　sequence　operators　are　investigated.　Their　potential　effect　in　smoothing　unexpected　disturbances　while　maintaining　the　normal　trend　in　sample　series　is　analyzed　and　compared　with　other　widely　used　sequence　operators　in　time　series　modeling.　Results　of　theoretical　and　empirical　research　show　that　the　proposed　novel　fractional　weakening　buffer　operators　are　effective　in　improving　the　development　pattern　analysis　of　time　series　in　disturbance　scenarios,　while　also　avoid　too　subjectively　weighting　experimental　data　from　collected　samples.　The　robust　of　the　proposed　operator-based　prediction　algorithm　against　noise　effect　is　tested　in　five　different　types　of　noise　scenarios.　Result　of　empirical　study　demonstrates　that　the　proposed　method　improves　the　series　prediction　performance　and　it　also　improves　the　robustness　of　corresponding　forecasting　algorithms.　These　unique　properties　of　the　proposed　weakening　buffer　operators　make　them　more　attractive　in　time　series　analysis.　©　2018　Elsevier　B.V.