改进三次指数平滑算法的数据库资源预测模型研究

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Research on Database Resource Prediction Model with Improved Triple Exponential Smoothing Algorithm

Qiu Yufeng，Liang Kehui， Xu Feng （ChinaUnionPay Co.，Ltd.，Shanghai 201201，China）

Abstract：Thetraditionaltripleexponentialsmoothingalgorithmiswidelyusedintrendprediction，butithaslimitationsin handlingsudnfluctuationsandmulti-periodcharacteristicsofdatabaseresoureeload：itssmothingoeficientsaretaticallyset andcannotbedynamicallyadjustedtoadapttorapiddatachanges，resultingininsuficientadaptabilitywhenfacingsharp fluctuationsinresourceusageorcomplexperiodicity.Toadressthisissue，thispaperproposesanimprovedtripleexponential smoothingalgorithminthecontextofdatabaseresourceprediction.Firstly，thealgorithmintegratestheSavitzky-Golayfiltering techniquetoefectivelyremovehigh-frequencynoisefromdataandensurethepurityoftheinputsignal.Meanwhile，itincorporates adynamicsmothingcoeficientmechanism，enablingthemodeltoadjustparametersinrealtime，flexiblyrespondtorandom fluctuations，accuratelycapturelong-termtrendchanges，andmakefulluseoftheinherentlawsofmulti-periodpatens.Such comprehensiveoptimizationnotonlyenhancesthemodel'srobustnessbutalsosignificantlyimprovestheacuracyofdatabasecloud resourceprediction.Ultimatelyitahieveshg-precisionresouceutilizationpredictionprovidingeliableandsientificsuportfor database cloud resource management decisions.

Keywords：TripleExponentialSmoothingAlgorithm；Savitzky-GolayFiltering；DynamicSmothingCoeficient;；DatabaseCloud Resource Forecasting

0引言

随着数字化转型的深入推进，数据库作为信息系统的核心数据存储与处理载体，其资源负载呈现出高频波动、非线性变化的复杂特征。（剩余7159字）