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KNIME Weather Data Cleaning and Model Training

KNIME Weather Data Cleaning and Model Training
After reading data, basic pre-processing is carried out. This includes steps like introducing missing time stamps and filling for corresponding temperaturevalues. Filtering rows for a reasonable number of records and reseting row numbers for better partitioning ahead. After partitioning rows, SARIMA model is trained with defined hyperparameters. In total readingsspannning up to almost 24 days were used and forecasts for next 33 hours were predicted.trained model was also extracted for deployment purposes. Both predictions and original values are plottedon a Line Plot, along with error metrics. This part reads data from Snowflake database and reads IoT data to be read in to KNIME. (p = 3, d=1, q=2) (P=3, D=1, Q=2) s = 24Select table with readingsRetreive result of table into KNIMEOverlay forecastand true valuesMinor TimestampPre-processingEstablish connection toDatabaseFilter rows for limited number of daysPredict on 33 hoursSelect top 550 rows for trainingReset row numbersIntroduce missingtimestampsFill in missingvalues using Linear InterpolationWrite modelfor deploymentAggregate temperaturereadings over each hour SARIMA Learner DB Table Selector DB Reader Visualize Forecast Convert toDate&Time Snowflake Connector Row Filter SARIMA Predictor Partitioning RowID Timestamp Alignment Missing Value Model Writer AggregationGranularity After reading data, basic pre-processing is carried out. This includes steps like introducing missing time stamps and filling for corresponding temperaturevalues. Filtering rows for a reasonable number of records and reseting row numbers for better partitioning ahead. After partitioning rows, SARIMA model is trained with defined hyperparameters. In total readingsspannning up to almost 24 days were used and forecasts for next 33 hours were predicted.trained model was also extracted for deployment purposes. Both predictions and original values are plottedon a Line Plot, along with error metrics. This part reads data from Snowflake database and reads IoT data to be read in to KNIME. (p = 3, d=1, q=2) (P=3, D=1, Q=2) s = 24Select table with readingsRetreive result of table into KNIMEOverlay forecastand true valuesMinor TimestampPre-processingEstablish connection toDatabaseFilter rows for limited number of daysPredict on 33 hoursSelect top 550 rows for trainingReset row numbersIntroduce missingtimestampsFill in missingvalues using Linear InterpolationWrite modelfor deploymentAggregate temperaturereadings over each hourSARIMA Learner DB Table Selector DB Reader Visualize Forecast Convert toDate&Time Snowflake Connector Row Filter SARIMA Predictor Partitioning RowID Timestamp Alignment Missing Value Model Writer AggregationGranularity

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Created by: Ali.Marvi
Created at: 2021-07-01
On NodePit since: 2021-09-10
Last update: 2024-04-27
Created with KNIME version: v4.4.0
Tags: sarimadataCleaningIoTpreprocessingtrainingpartitioning

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