Machine Learning – KNIME Community Hub

Search

Workflow

Machine Learning

Time Series Prediction Energy Usage Random Forest Lag

Draft Latest edits on

Dec 30, 2021 7:47 AM

Drag & drop

Like

Download workflow

Workflow preview

This workflow demonstrates how to predict time series (energy consumption) by a Random Forest model using lagged values as predictors.

External resources

Time Series Analysis with Components
The Lag Column Node
All you need is ... the Lag Column Node!

Loading deploymentsLoading ad hoc jobs

Used extensions & nodes

Extensions Nodes

Created with KNIME Analytics Platform version 4.5.0

Go to item
KNIME Base nodes
KNIME AG, Zurich, Switzerland
Version 4.5.0
knime
Go to item
KNIME Ensemble Learning Wrappers
KNIME AG, Zurich, Switzerland
Version 4.5.0
knime
Go to item
KNIME Expressions
KNIME AG, Zurich, Switzerland
Version 4.5.0
knime
Go to item
KNIME JavaScript Views
KNIME AG, Zurich, Switzerland
Version 4.5.0
knime
Go to item
KNIME Javasnippet
KNIME AG, Zurich, Switzerland
Version 4.5.0
knime
Go to item
KNIME Math Expression (JEP)
KNIME AG, Zurich, Switzerland
Version 4.5.0
knime
Go to item
KNIME Python Integration
KNIME AG, Zurich, Switzerland
Version 4.5.0
knime
Go to item
KNIME Quick Forms
KNIME AG, Zurich, Switzerland
Version 4.5.0
knime

Go to item
Go to item
Go to item
Go to item
Go to item
Go to item

Legal

By using or downloading the workflow, you agree to our terms and conditions.

KNIME
Open for Innovation

KNIME AG
Talacker 50
8001 Zurich, Switzerland

Software
Getting started
Documentation
Courses + Certification
Solutions

KNIME Hub
KNIME Forum
Blog
Events

Partner
Developers

KNIME Home
Careers
Contact us

Download KNIME Analytics Platform Read more about KNIME Business Hub

© 2024 KNIME AG. All rights reserved.

Trademarks
Imprint
Privacy
Terms & Conditions
Data Processing Agreement
Credits