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P2.2.3

Comparison of auto-regressive model settings 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 - Observe predictive performance for different orders of auto-regresion
Select rowID and cluster_26 columns
Column Filter
Introduce missing hours
Timestamp Alignment
plot real vs. predicted
Line Plot (JavaScript) (legacy)
Estimate ARIMA parameters
ARIMA Learner
Assess performance
ARIMA Predictor
plot real vs. predicted
Line Plot (JavaScript) (legacy)
Report performance
Numeric Scorer
Assess performance
ARIMA Predictor
Estimate ARIMA parameters
ARIMA Learner
plot real vs. predicted
Line Plot (JavaScript) (legacy)
convert date/time into Date&Time objects
String to Date&Time (deprecated)
Estimate ARIMA parameters
ARIMA Learner
Select rowID and cluster_26 columns
Column Filter
Report performanec
Numeric Scorer
90% for training 10% for testing
Table Partitioner
Assess performance
ARIMA Predictor
Introduce missing hours
Timestamp Alignment
Estimate ARIMA parameters
ARIMA Learner
Interpolate missing values
Missing Value
Report performanec
Numeric Scorer
Select a subset of datato reduce computational demand
Row Filter
Report performance
Numeric Scorer
Assess performance
ARIMA Predictor
Assess performance
ARIMA Predictor
Table Partitioner
plot real vs. predicted
Line Plot (JavaScript) (legacy)
CSV Reader
Interpolate missing values
Missing Value
plot real vs. predicted
Line Plot (JavaScript) (legacy)
ARIMA Predictor
Estimate ARIMA parameters
ARIMA Learner
ARIMA Learner
convert date/time into Date&Time objects
String to Date&Time (deprecated)
Report performanec
Numeric Scorer
energy usage data
CSV Reader

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Created by: chris
Created at: 2021-05-19
On NodePit since: 2026-04-24
Last update: 2026-04-24
Created with KNIME version: v5.8.3

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