PACF

PACF (partial autocorrelation function) gives the partial correlation between a sequence and its lagged values, controlling for the values of lags that are shorter than its own. See the StatsModels documentation at http://www.statsmodels.org/stable/generated/statsmodels.tsa.stattools.pacf.html for more information.

Parameters

• The k parameter specifies the number of lags to return partial autocorrelation for. By default k is 40.
• The method parameter specifies which method for the calculation to use. By default method is unbiased.
• The conf_interval parameter specifies the confidence interval in percentage to return. By default conf_interval is set to 95.

Syntax

fit PACF <field> [k=<int>] [method=<ywunbiased|ywmle|ols>] [conf_interval=<int>]

Example

The following example uses PACF (partial autocorrelation function) on a test set.

... | fit PACF logins k=20 conf_interval=90

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ON THIS PAGE Permalink to this section#

• Algorithms in the AI Toolkit - ML-SPL Quick Reference Guide
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  • Utility Algorithms - ACF (autocorrelation function)
    • PACF (partial autocorrelation function)

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Source: /en/splunk-cloud-platform/apply-machine-learning/use-ai-toolkit/5.6.4/algorithms-and-scoring-metrics-in-the-ai-toolkit/algorithms-in-the-ai-toolkit (upstream Splunk AITK 5.6.4 docs)

Local availability Permalink to this section#

• Local class: PACF
• Source file: Splunk_ML_Toolkit/bin/algos/PACF.py (in-repo path Splunk_ML_Toolkit/bin/algos/PACF.py)
• algos.conf stanza: [PACF]
• Class bases: ACF

Class docstring Permalink to this section#

Compute partial autocorrelation function.

Source Permalink to this section#

Adapted from the Splunk AI Toolkit 5.6.4 documentation at /en/splunk-cloud-platform/apply-machine-learning/use-ai-toolkit/5.6.4/algorithms-and-scoring-metrics-in-the-ai-toolkit/algorithms-in-the-ai-toolkit (section: preprocessor).