87 results
- Trains an AutoRegressive Integrated Moving Average (ARIMA) model. ARIMA model captures temporal structures in time series data in the following components: - AR: Relationship between the current obse…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
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- Trains AutoRegressive Integrated Moving Average (ARIMA) models and returns the best model according to the search criterion (AIC, BIC) within the provided constraints (max p,d,q). ARIMA model capture…Component
- This component restores seasonality (1st and 2nd) and trend into the forecasted residual series. The trend model, the seasonal components, and the lags where the seasonal peaks occur have been obtain…Component
- Restore trend into time series forecasts. The trend model has been obtained from the training data based on the row index.Component
- This component restores seasonality into forecasted time series based on the seasonality column that has been extracted from the training data and the lag value where the seasonality occurs.Component
- This component aggregates values in a selected numeric or string column by timestamps extracted from a column of type Date&Time. The granularity of the timestamps and the aggregation method are defin…Component
- Decomposes selected Time-Series or IoT signal into Trend, 2 Seasonal Components, and the remaining Residual. Signal = T + S1 + S2 + R [T] Trend Component: is calculated by fitting a regression model …Component
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- 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
- 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
- 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 analyzes the residuals of an ARIMA (AutoRegressive Integrated Moving Average) model by 1. visualizing auto correlation of the residuals 2. performing Ljung-Box test of autocorrelation …Component
- This component provides three different ways for calculating and visualizing the ARR: 1. calculating the total ARR in each month and visualizing it in a line plot 2. calculating the total ARR in each…Component
- This node performs threshold analysis for binary classification or retrieval results with confidence values. It allows to calculate Accuracy, Precision, Recall and F1 measures depending on varying th…Component
- This component outputs an interactive dashboard that provides a detailed analysis of airline dataset in terms of Overview, Exploratory Data Analysis, Model Building and InterpretabilityComponent
- This Component is able to create a Local Interpretable Model-agnostic Explanation (LIME) to explain the predictions of any machine learning model in KNIME. You have to use this component together wit…Component
- The Component uses IDs from several data sources to create an interactive pathway analysis based on the web service of Reactome. The input for the pathway enrichment analysis can either be a column t…Component
- Trains AutoRegressive Integrated Moving Average (ARIMA) models and returns the best model according to the search criterion (AIC, BIC) within the provided constraints (max p,d,q). ARIMA model capture…Component