Hi All, I intend to use XGBoost on Time-series data. It appears that Time-series data needs to be made suitable for Supervised ML first by doing feature engineering. As such, I need some example workflows on Time-series data where XGBoost has been used.

This workflow doesn’t employ XGBoost, but does have a detailed discussion of data prep. It may help you. [image] Time Series Analysis with KNIME — An introduction Learn the basic steps to perform no-code time series analysis with KNIME Analytics Platform and still leverage Python libraries Reading time: 8 min read

[image] rfeigel: have a detailed discussion of data prep Data prep for ML? Because the data needs to be treated differently for time-series analysis vs ML.

Without knowing anything about your data or business case its hard offer specific suggestions. Look at this: [image] xgboost regression hyperparameter optimization KNIME Analytics Platform hi dear form users ı m new to knime platform. ı have a quaestions. ı want to predict by using xgboost parameters={ "learning_rate":[0.1,0.01,0.001], "n_estimators":range(100, 1200, 100), } xgb_model = xg.XGBRegressor(random_state=0) this is my pyhton code and ı m searc by GridSearch to hyperparameter . how can ı do this on knime ? [image] XGBoost For Time Series Forecasting: Don’t Use It Blindly Forecasting techniques don’t work well with all time series Reading time: 8 min read

Example Workflows of Supervised Machine Learning on Time-series data?

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rfeigel April 1, 2024, 2:31am 4

Without knowing anything about your data or business case its hard offer specific suggestions. Look at this: