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P2.2.2

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: in this case a linear regression model
convert date/time into Date&Time objects
String to Date&Time (deprecated)
Use the learned linear regression moel for prediction over test data
Regression Predictor
from x(t) to: x(t), x(t-1), x(t-2), ..., x(t-lag)
Lag Column
cluster(t) as target columns with past of cluster(t) as input
Linear Regression Learner
Performance reporting
Numeric Scorer
Prepare cluster_26, row IDand predicted values for visualisation
Column Filter
90% for trainint 10% for testing
Table Partitioner
cluster(t) as target columns with past of cluster(t) as input
Linear Regression Learner
real vs. predicted
Line Plot (JavaScript) (legacy)
Select rowID and cluster_26 columns
Column Filter
Select rowID and cluster_26 columns
Column Filter
from x(t) to: x(t), x(t-1), x(t-2), ..., x(t-lag)
Lag Column
Interpolate missing values
Missing Value
Introduce missing hours
Timestamp Alignment
Interpolate missing values
Missing Value
from x(t) to: x(t), x(t-1), x(t-2), ..., x(t-lag)
Lag Column
Performance reporting
Numeric Scorer
90% for trainint 10% for testing
Table Partitioner
Select rowID and cluster_26 columns
Column Filter
Use the learned linear regression moel for prediction over test data
Regression Predictor
from x(t) to: x(t), x(t-1), x(t-2), ..., x(t-lag)
Lag Column
from x(t) to: x(t), x(t-1), x(t-2), ..., x(t-lag)
Lag Column
Performance reporting
Numeric Scorer
energy usage data
CSV Reader
energy usage data
CSV Reader
convert date/time into Date&Time objects
String to Date&Time (deprecated)
Use the learned linear regression moel for prediction over test data
Regression Predictor
energy usage data
CSV Reader
real vs. predicted
Line Plot (JavaScript) (legacy)
Prepare cluster_26, row IDand predicted values for visualisation
Column Filter
90% for trainint 10% for testing
Table Partitioner
from x(t) to: x(t), x(t-1), x(t-2), ..., x(t-lag)
Lag Column
real vs. predicted
Line Plot (JavaScript) (legacy)
Prepare cluster_26, row IDand predicted values for visualisation
Column Filter
Interpolate missing values
Missing Value
cluster(t) as target columns with past of cluster(t) as input
Linear Regression Learner
convert date/time into Date&Time objects
String to Date&Time (deprecated)
Introduce missing hours
Timestamp Alignment
Introduce missing hours
Timestamp Alignment

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Extensions

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

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Created by: chris
Created at: 2021-05-18
On NodePit since: 2026-04-24
Last update: 2026-04-24
Created with KNIME version: v5.8.3

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