Hub
  • Software
  • Blog
  • Forum
  • Events
  • Documentation
  • About KNIME
  • KNIME Hub
  • knime
  • Spaces
  • Education
  • Courses
  • L4-TS Introduction to Time Series Analysis
  • Solutions
  • Session_2
  • 02_Inspect_and_Remove_Seasonality
WorkflowWorkflow

Solution to the Exercise 2: Inspecting and Removing Seasonality

Time Series Energy Usage Seasonality Education

Last edited: 

Drag & drop
Like
Download workflow
Copy short link
Workflow preview
This workflow shows the seasonality of time series (energy consumption) in an autocorrelation plot. The seasonality is removed by differencing the time series at the lag where the maximum peak is detected in the autocorrelation plot. As an alternative approach, the time series is decomposed into its trend, first and second seasonalities, and residual. The distribution of energy consumption for each hour is shown for both the original and differenced time series.

External resources

  • Slides on the KNIME Website
  • Extract Date&Time Fields Node

Used extensions & nodes

Created with KNIME Analytics Platform version 4.4.0
  • Go to item
    KNIME Base nodes Trusted extension

    KNIME AG, Zurich, Switzerland

    Version 4.4.0

  • Go to item
    KNIME Expressions Trusted extension

    KNIME AG, Zurich, Switzerland

    Version 4.4.0

  • Go to item
    KNIME JavaScript Views Trusted extension

    KNIME AG, Zurich, Switzerland

    Version 4.4.0

  • Go to item
    KNIME Javasnippet Trusted extension

    KNIME AG, Zurich, Switzerland

    Version 4.4.0

  • Go to item
    KNIME Math Expression (JEP) Trusted extension

    KNIME AG, Zurich, Switzerland

    Version 4.4.0

  • Go to item
    KNIME Python Integration Trusted extension

    KNIME AG, Zurich, Switzerland

    Version 4.4.0

  • Go to item
    KNIME Quick Forms Trusted extension

    KNIME AG, Zurich, Switzerland

    Version 4.4.0

  1. Go to item
  2. Go to item
  3. Go to item
  4. Go to item
  5. Go to item
  6. Go to item

Legal

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

Discussion
Discussions are currently not available, please try again later.

KNIME
Open for Innovation

KNIME AG
Hardturmstrasse 66
8005 Zurich, Switzerland
  • Software
  • Getting started
  • Documentation
  • E-Learning course
  • Solutions
  • KNIME Hub
  • KNIME Forum
  • Blog
  • Events
  • Partner
  • Developers
  • KNIME Home
  • KNIME Open Source Story
  • Careers
  • Contact us
Download KNIME Analytics Platform Read more on KNIME Server
© 2022 KNIME AG. All rights reserved.
  • Trademarks
  • Imprint
  • Privacy
  • Terms & Conditions
  • Credits