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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.

Used extensions & nodes

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

    KNIME AG, Zurich, Switzerland

    Version 4.5.2

    knime
  • KNIME Data Generation Trusted extension

    KNIME AG, Zurich, Switzerland

    Version 4.5.0

    knime
  • KNIME Excel Support Trusted extension

    KNIME AG, Zurich, Switzerland

    Version 4.5.2

    knime
  • KNIME Expressions Trusted extension

    KNIME AG, Zurich, Switzerland

    Version 4.5.1

    knime
  • KNIME JavaScript Views Trusted extension

    KNIME AG, Zurich, Switzerland

    Version 4.5.2

    knime
  • KNIME Javasnippet Trusted extension

    KNIME AG, Zurich, Switzerland

    Version 4.5.0

    knime
  • KNIME Math Expression (JEP) Trusted extension

    KNIME AG, Zurich, Switzerland

    Version 4.5.0

    knime
  • KNIME Python Integration Trusted extension

    KNIME AG, Zurich, Switzerland

    Version 4.5.2

    knime
  • KNIME Quick Forms Trusted extension

    KNIME AG, Zurich, Switzerland

    Version 4.5.2

    knime
  • KNIME Statistics Nodes Trusted extension

    KNIME AG, Zurich, Switzerland

    Version 4.5.0

    knime
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