Time Series Generators

Basic Building Blocks

These generators create fundamental time series patterns used in performance testing scenarios.

A constant time series: S = x, x, x, x...

Represents an ideal performance test with no variation.

Normally distributed noise: S = x1, x2, x3... where X ~ N(mean, sigma)

Represents typical performance test output with random variation.

Uniformly distributed noise (white noise): random(min, max)

Single deviating point (anomaly): S = x, x, x, x, x, x', x, x...

Single change point: S = x1, x1, x1, x2, x2, x2...

Represents a performance regression or improvement that persists.

Temporary regression: S = x1, x1... x2, ...x2, x3, x3...

Oscillation between two values: S = x1, x2, x2, x1, x2, x1...

Constant mean, changing variance: S = N(mean, sigma1)..., N(mean, sigma2)...

Constant mean and variance, but phase shifts: S = cos(x)..., sin(x)...

Multiple consecutive changes: S = x0, x0... x1, x2, ... xn, xn...