Inspect Seasonality

This component calculates autocorrelation with Pearson Correlation for lagged copies of time series. Additionally, it produces an interactive view that displays the Autocorrelation Function (ACF) Plot, Partial Autocorrelation Function (PACF) Plot, and detects the first local maximum of correlation for sign of dominant seasonality.

Required extensions:
KNIME Python Integration
(https://hub.knime.com/knime/extensions/org.knime.features.python2/latest)
KNIME Quick Forms
(https://hub.knime.com/knime/extensions/org.knime.features.js.quickforms/latest)
KNIME JavaScript Views
(https://hub.knime.com/knime/extensions/org.knime.features.js.views/latest)
KNIME Math Expression (JEP)
(https://hub.knime.com/knime/extensions/org.knime.features.ext.jep/latest)

Options

Value Column
Column to apply (P)ACF analysis to. This column has to be a numeric column.
Seasonality Cut Off
The component will only consider correlations above the cut off values as seasonal trends. This value has to be between 0 and 1.
Max Lag
Maximum lag to use when checking for (partial) autocorrelation.
Step size of the moving ACF window
Size of steps to move when checking (partial) autocorrelation of different lags. This value should not exceed the value of Max Lag.

Input Ports

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Data containing selected column for the Autocorrelation Function (ACF) and Partial Autocorrelation Function (PACF) analysis.

Output Ports

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This table contains a list of detected local maximum, their corresponding correlation, and lag value.
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This flow variable contains the lag value where the dominant seasonality might occur.

Nodes

Extensions

Links

Download

To use this component in KNIME, download it from the below URL and open it in KNIME:

Download Component
On NodePit since: 2022-06-07
Last update: 2024-11-23
Created with KNIME version: v4.2.0

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