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02_​Example_​for_​Predicting_​Time_​Series.knwf

Simple auto-regressive model to predict a time series

- Simple means just raw data: no seasonality correction, stationarity assumption
- Auto means usage of past of the same time series for prediction. No other exogenous time series/data used.
- Regressive refers to the model: either a linear or a polynomial regression

Simple auto-regressive model to predict a time series - Simple means just raw data: no seasonality correction, stationarity assumption - auto means usage of past of the same time series for prediction. No other exogenous time series/data used. - Regressive refers to the model: either a linear or a polynomial regression polynomial AR(lag)linear AR(lag)Here is where the time series gets selected andrenamed to "cluster"from x(t) to: x(t), x(t-1), x(t-2), ..., x(t-lag)read clusteredtime seriesreal vs. predictedreal vs. predictedfrom x(t) to: x(t), x(t-1), x(t-2), ..., x(t-lag)real vs. predictedNode 214Node 215 ARIMA Learner Partitioning Poly Regression Linear Regression Prepare Data Lag Column File Reader Line Plot Line Plot Lag Column ARIMA Predictor Line Plot Concatenate Row Filter Simple auto-regressive model to predict a time series - Simple means just raw data: no seasonality correction, stationarity assumption - auto means usage of past of the same time series for prediction. No other exogenous time series/data used. - Regressive refers to the model: either a linear or a polynomial regression polynomial AR(lag)linear AR(lag)Here is where the time series gets selected andrenamed to "cluster"from x(t) to: x(t), x(t-1), x(t-2), ..., x(t-lag)read clusteredtime seriesreal vs. predictedreal vs. predictedfrom x(t) to: x(t), x(t-1), x(t-2), ..., x(t-lag)real vs. predictedNode 214Node 215 ARIMA Learner Partitioning Poly Regression Linear Regression Prepare Data Lag Column File Reader Line Plot Line Plot Lag Column ARIMA Predictor Line Plot Concatenate Row Filter

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On NodePit since: 2022-02-06
Last update: 2026-01-06
Created with KNIME version: v4.1.3

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