通过时间序列分析为人工智能赋能.pptx

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1、Empowering AI Through Time Series AnalysisSalochina Oad,PhD Usxpress.IncSAgenda Why do we need time series analysis?What are the key expectations before starting forecasting?How to deal with variation of time series data?How can time series empower AI?The value of improving the performance of AI Mod

2、els Challenges in detecting and preventing fraudulent activitiesModelsBehavioral AnalyticsReal-time monitoring&alerts Model training&evaluationEfficiency&performanceTime seriesTrend/Seasonal Residuals SignalNoiseTime seriesMultiplicative TSAdditive TSTypes of time series data Time seriesDateCompanyC

3、losetCloset-112-27-2022Google87.9312-28-2022Google86.4687.9312-29-2022Google88.9586.4612-30-2022Google88.7388.9501-03-2023Google89.7088.7301-04-2023Google88.7189.7001-05-2023Google86.7788.7101-06-2023Google88.1686.77Pooled/Panel DateCompanyClosetCloset-112-27-2022Google87.9312-28-2022Google86.4687.9

4、312-29-2022Google88.9586.4612-30-2022Google88.7388.9512-27-2022AAPL89.7012-28-2022AAPL88.7189.7012-29-2022AAPL86.7788.7112-30-2022AAPL88.1686.77Time series use casesDemand forecast How many order a trucking company got this week-Cost planningSales forecastHow much revenue was generated serving speci

5、fic customer.-Financial outcomesRoadmap-Forecasting projectDetermine TargetHorizon of the forecastGather dataDevelop a modelDeploy to productionMonitor Time series pipelineETLEDAPreprocessingForecastingDiagnostics Read Impute Sampling Visualization StatisticsDetrendDestationariesFeature-EngineeringP

6、arametric-ModelsNonparametric-Models Autocorrelation Stationarity Normality ResidualsEDA-Components of a time seriesTrendSeasonal componentResiduals Other elements Holidays AnomaliesAirline passenger EDA-Components of a time seriesTrendSeasonal componentResiduals Other elements Holidays AnomaliesTre