KNIME Weather Data Cleaning and Model Training

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This workflows reads data from Snowflake database containing sensor readings. Seconds are removed in time stamps and temperature readings are averaged over the hour. Missing timestamps are introduced and filled using Linear Interpolation. Reasonable number of rows are considered for training and testing. Data is partitioned and SARIMA model is trained, finally results are visualized in the component along with original values.

URL: S.A.R.I.M.A. Seasonal ARIMA Models with KNIME https://www.knime.com/blog/sarima-seasonal-arima-models-with-knime
URL: An Introduction to Integrated Deployment https://www.knime.com/blog/integrated-deployment-blog-series-episode-1-an-introduction-to-integrated-deployment

Nodes

• Rule Engine Variable10 ×
• Table Row to Variable9 ×
• Column Filter8 ×
• GroupBy8 ×
• Integer Configuration8 ×
• Show all 59 nodes

Extensions

• FeatureKNIME Base nodes
• FeatureKNIME Conda Integration
• FeatureKNIME Data Generation
• FeatureKNIME Database
• FeatureKNIME JavaScript Views
• FeatureKNIME JavaScript Views (Labs)
• FeatureKNIME Javasnippet
• FeatureKNIME Math Expression (JEP)
• FeatureKNIME Python 2 Integration (legacy)
• FeatureKNIME Quick Forms
• FeatureKNIME Snowflake Integration
• FeatureKNIME Statistics Nodes

Links

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