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Accessing, Transforming and Modeling Time Series

Time series ARIMA Granularity Trend Seasonality
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This workflow shows how to access time series data, make it equally-spaced, impute missing values, aggregate it at a greater granularity, and explore it visually. After these steps, the time series is decomposed into trend, seasonality, and residual. The residual is modeled with an ARIMA model, and deployment data are saved for testing the model's out-of-sample forecast accuracy.

External resources

  • Time Series Analysis with Components
  • Building a Time Series Application
  • Samle-Superstore.xls data on Kaggle

Used extensions & nodes

Created with KNIME Analytics Platform version 4.5.2
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    KNIME Base nodes Trusted extension

    KNIME AG, Zurich, Switzerland

    Version 4.5.2

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    knime
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    KNIME Data Generation Trusted extension

    KNIME AG, Zurich, Switzerland

    Version 4.5.0

    KNIME profile image
    knime
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    KNIME Excel Support Trusted extension

    KNIME AG, Zurich, Switzerland

    Version 4.5.2

    KNIME profile image
    knime
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    KNIME Expressions Trusted extension

    KNIME AG, Zurich, Switzerland

    Version 4.5.1

    KNIME profile image
    knime
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    KNIME JavaScript Views Trusted extension

    KNIME AG, Zurich, Switzerland

    Version 4.5.2

    KNIME profile image
    knime
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    KNIME Javasnippet Trusted extension

    KNIME AG, Zurich, Switzerland

    Version 4.5.0

    KNIME profile image
    knime
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    KNIME Math Expression (JEP) Trusted extension

    KNIME AG, Zurich, Switzerland

    Version 4.5.0

    KNIME profile image
    knime
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    KNIME Python Integration Trusted extension

    KNIME AG, Zurich, Switzerland

    Version 4.5.2

    KNIME profile image
    knime
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    KNIME Quick Forms Trusted extension

    KNIME AG, Zurich, Switzerland

    Version 4.5.2

    KNIME profile image
    knime
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    KNIME Statistics Nodes Trusted extension

    KNIME AG, Zurich, Switzerland

    Version 4.5.0

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    knime
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