| # Time Series Generators |
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| ## Basic Building Blocks |
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| These generators create fundamental time series patterns used in performance |
| testing scenarios. |
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| ### Constant |
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| A constant time series: `S = x, x, x, x...` |
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| Represents an ideal performance test with no variation. |
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| ### Noise (Normal) |
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| Normally distributed noise: `S = x1, x2, x3...` where `X ~ N(mean, sigma)` |
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| Represents typical performance test output with random variation. |
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| ### Noise (Uniform) |
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| Uniformly distributed noise (white noise): `random(min, max)` |
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| ### Outlier |
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| Single deviating point (anomaly): `S = x, x, x, x, x, x', x, x...` |
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| ### Step Function |
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| Single change point: `S = x1, x1, x1, x2, x2, x2...` |
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| Represents a performance regression or improvement that persists. |
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| ### Regression + Fix |
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| Temporary regression: `S = x1, x1... x2, ...x2, x3, x3...` |
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| ## Advanced Phenomena |
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| ### Banding |
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| Oscillation between two values: `S = x1, x2, x2, x1, x2, x1...` |
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| ### Variance Change |
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| Constant mean, changing variance: `S = N(mean, sigma1)..., N(mean, sigma2)...` |
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| ### Phase Change |
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| Constant mean and variance, but phase shifts: `S = cos(x)..., sin(x)...` |
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| ### Multiple Changes |
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| Multiple consecutive changes: `S = x0, x0... x1, x2, ... xn, xn...` |
