244 results
- Computes predictions from an estimated AutoRegressive Integrated Moving Average (ARIMA) model. Two types of predictions are computed: 1. Forecast: forecast of the given time series h periods ahead. 2…Component
- Displays the labeled cluster centers.Component
- Image preprocessing for VGG neural network. The node expects a flow variable "currentColumnName" to define the column, which has to be preprocessed. Mimic "preprocess_input" from https://github.com/k…Component
- Removes seasonality trend in input data. Required extensions: KNIME Quick Forms (https://hub.knime.com/knime/extensions/org.knime.features.js.quickforms/latest) KNIME Math Expression (JEP) (https://h…Component
- This component clusters the customers into 4 groups.Component
- Removes seasonality trend in input data. Required extensions: KNIME Quick Forms (https://hub.knime.com/knime/extensions/org.knime.features.js.quickforms/latest) KNIME Math Expression (JEP) (https://h…Component
- This component calculates autocorrelation with Pearson Correlation for lagged copies of time series. Additionally, it produces an interactive view that displays the Autocorrelation Function (ACF) Plo…Component
- This component checks whether the selected timestamp column is uniformly sampled in the selected time scale. Missing values will be inserted at skipped sampling times. Required extensions: KNIME Quic…Component
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- This Component should be nested within another Component to generate a flow chart header at the top of the Composite View. Such flow chart will help the business analyst on the KNIME WebPortal to und…Component
- This component copies column values from preceding rows into the current row in a Spark DataFrame/RDD. The component can be used to - make a copy of the selected column and shift the cells I steps up…Component
- This Component should be nested within another Component to generate a flow chart header at the top of the Composite View. Such flow chart will help the business analyst on the KNIME WebPortal to und…Component
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- This component implements substructure and similarity searches through ChEMBLdb using its API. More information can be found here: ChEMBL webservices: https://chembl.gitbook.io/chembl-interface-docum…Component
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- The Document Similarity Predictor applies the model obtained by the Document Similarity Learner to a test document. It computes the cosine similarity between the original corpus of documents table an…Component
- This component visualize the data of one cluster and allows to define a label.Component
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- Calculates k-period log return series from one-period log return series using the following formula: r(k) = r_1+r_2+...+r_k Missing values are treated as zeros. Required extensions: - KNIME Quick For…Component