Click on each picture to find out about it on X. ARIMA modelsAdvantages of ProphetImbalanced dataSteps in DS project2D graphsBest scenarios for ProphetTime Series with XGBoostFeature selectionGranger causalitySMOTE for imbalanced dataNature of missing dataTime biasSources of Missing dataTest for Granger causalityMinMax ScalingFeature scalingModel doesn't behave well with real dataWhat kind of Time Series model is Prophet?Why should you split your datasetIntroduction to RNNsTime Series models classificationClean your time series dataHandle Missing ValuesSimple Exponential SmoothingDouble Exponential SmoothingPrediction Direction AccuracyAdvantages of RNNsHow to address imbalanced data?Forecast BiasForecast Interval Coverage as a Performance MetricSelect your Time Series modelRecurrent Neural NetworksExample of nature of missing dataBox-Jenkins methodologyReframe Time Series data into Supervised Learning Architecture of RNNsWhen not to use Prophet?Missing data is a problemTriple Exponential Smoothing . . . . . . . . . . . . . . . . Follow me on X