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02_​Stock-model

02_Stock_model
Stock Analysis: Modeling workflowThis workflow receives the input from the STOCKS workflow via the Contained Input (Table) node.In the Feature Creation metanode, variables are extracted based on Date and Lag variables. The Variable selection Loop metanode selects the combination of variables with the most accurate prediction model(using the lowest mean average percentage error (MAPE) criterion). This process uses the last 60 days of data as the validation sample.The selected variables are used to estimate a Linear Regression model, which is scored for the Score Sample (t +1). This prediction is merged with the model evaluation measures for the past 30 days and exportedto the STOCKS workflow via the Container Output (Table) node. Predict stock valueSplit Train/ScoresamplesEstimate modelSelect features fromvariable selection loopFilter unnecessary columnsCompute % changeReceived inputfrom STOCKS workflowExport result toSTOCKS workflowJoin prediction withevaluation metricsCreatedemo data Feature Creation RegressionPredictor Row Splitter Linear RegressionLearner Feature SelectionFilter VariableSelection Loop Column Filter Math Formula ContainerInput (Table) ContainerOutput (Table) Cross Joiner Collect ModelEvaluation Metrics Table Creator Stock Analysis: Modeling workflowThis workflow receives the input from the STOCKS workflow via the Contained Input (Table) node.In the Feature Creation metanode, variables are extracted based on Date and Lag variables. The Variable selection Loop metanode selects the combination of variables with the most accurate prediction model(using the lowest mean average percentage error (MAPE) criterion). This process uses the last 60 days of data as the validation sample.The selected variables are used to estimate a Linear Regression model, which is scored for the Score Sample (t +1). This prediction is merged with the model evaluation measures for the past 30 days and exportedto the STOCKS workflow via the Container Output (Table) node. Predict stock valueSplit Train/ScoresamplesEstimate modelSelect features fromvariable selection loopFilter unnecessary columnsCompute % changeReceived inputfrom STOCKS workflowExport result toSTOCKS workflowJoin prediction withevaluation metricsCreatedemo data Feature Creation RegressionPredictor Row Splitter Linear RegressionLearner Feature SelectionFilter VariableSelection Loop Column Filter Math Formula ContainerInput (Table) ContainerOutput (Table) Cross Joiner Collect ModelEvaluation Metrics Table Creator

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Created by: maartenm
Created at: 2019-04-10
On NodePit since: 2020-08-26
Last update: 2024-11-21
Created with KNIME version: v4.0.1
Tags: Innovation NoteStock AnalysisStock PredictionFeature Creation

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