series_pearson_correlation

The series_pearson_correlation function calculates the Pearson correlation coefficient between two numeric dynamic arrays (series). This measures the linear relationship between the two series, returning a value between -1 and 1, where 1 indicates perfect positive correlation, -1 indicates perfect negative correlation, and 0 indicates no linear correlation.

You can use series_pearson_correlation when you need to measure the strength and direction of linear relationships between time-series datasets. This is particularly useful for identifying related metrics, detecting causal relationships, validating hypotheses about system behavior, or finding leading indicators of performance issues.

Usage

Syntax

series_pearson_correlation(series1, series2)

Parameters

Returns

A numeric value between -1 and 1 representing the Pearson correlation coefficient:

• 1: Perfect positive linear correlation
• 0: No linear correlation
• -1: Perfect negative linear correlation

Use case examples

['sample-http-logs']
| extend city1 = iff(['geo.city'] == 'Tokyo', req_duration_ms, 0)
| extend city2 = iff(['geo.city'] == 'Nagasaki', req_duration_ms, 0)
| summarize tokyo_times = make_list(city1), nagasaki_times = make_list(city2)
| extend correlation = series_pearson_correlation(tokyo_times, nagasaki_times)
| project correlation

['otel-demo-traces']
| extend duration_ms = duration / 1ms
| extend frontend_dur = iff(['service.name'] == 'frontend', duration_ms, 0)
| extend checkout_dur = iff(['service.name'] == 'checkout', duration_ms, 0)
| summarize frontend = make_list(frontend_dur), checkout = make_list(checkout_dur)
| extend correlation = series_pearson_correlation(frontend, checkout)
| project correlation

['sample-http-logs']
| extend success_count = iff(status == '200', 1, 0)
| extend failure_count = iff(status == '500', 1, 0)
| summarize successes = make_list(success_count), failures = make_list(failure_count) by bin(_time, 1h)
| extend correlation = series_pearson_correlation(successes, failures)
| project correlation

List of related functions

• series_magnitude: Calculates the magnitude of a series. Use when you need vector length instead of correlation.
• series_stats: Returns comprehensive statistics. Use when you need variance and covariance components separately.
• series_subtract: Performs element-wise subtraction. Often used to compute deviations before correlation analysis.
• series_multiply: Performs element-wise multiplication. Use for weighted combinations instead of correlation.

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