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

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
from x(t) to: x(t), x(t-1), x(t-2), ..., x(t-lag)
Lag Column
real vs. predicted
Line Plot (JavaScript) (legacy)
read clustered time series
File Reader (deprecated)
real vs. predicted
Line Plot (JavaScript) (legacy)
polynomial AR(lag)
Poly Regression
linear AR(lag)
Linear Regression
from x(t) to: x(t), x(t-1), x(t-2), ..., x(t-lag)
Lag Column
Here is where the time series gets selected and renamed to "cluster"
Prepare Data
Table Partitioner

Nodes

Extensions

Links

Download

To use this workflow in KNIME, download it from the below URL and open it in KNIME:

Download Workflow
On NodePit since: 2026-03-09
Last update: 2026-03-29
Created with KNIME version: v5.8.2

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