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CI_​IA_​Inventory_​Outlier_​Detection

The aim of this flow is to exhaustively analyse significant or unusual variations in the movements and unit value of items along several axes of analysis.
We transform the categorical variables into numerical values using one-hot encoding. In this way, we facilitate their manipulation using the isolation forest algorithm. Using this algorithm, we identify anomalies.

Learn and predict Reporting Read and Preprocess data To KNIMETableClassificationExporting resultsStock movements H2O to Table H2O IsolationForest Learner H2O IsolationForest Predictor Rule Engine Denormalizer Preprocess Data Excel Writer Excel Reader Learn and predict Reporting Read and Preprocess data To KNIMETableClassificationExporting resultsStock movementsH2O to Table H2O IsolationForest Learner H2O IsolationForest Predictor Rule Engine Denormalizer Preprocess Data Excel Writer Excel Reader

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Created by: Maarit Laukkanen
Created at: 2019-02-25
On NodePit since: 2023-08-27
Last update: 2026-01-10
Created with KNIME version: v4.7.2
Tags: Inventoryitem outlier detectioninternal controlisolation foreststocks

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