Functions for piecewise regression on time series data
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Latest commit 86a0bdf Mar 29, 2018


This repo accompanies Piecewise regression: when one line simply isn’t enough, a blog post about Datadog's approach to piecewise regression. The code included here is intended to be minimal and readable; this is not a Swiss Army knife to solve all variations of piecewise regression problems.

Installation & dependencies

This package was written to work with both Python 2 and Python 3.

To install this package using setup tools, clone this repo and run python install from within the piecewise root directory.

The package's core piecewise() function for regression requires only numpy. The use of plot_data_with_regression() for plotting depends also on matplotlib.


Start by preparing your data as list-likes of timestamps (independent variables) and values (dependent variables).

import numpy as np

t = np.arange(10)
v = np.array(
    [2*i for i in range(5)] +
    [10-i for i in range(5, 10)]
) + np.random.normal(0, 1, 10)

Now, you're ready to import the piecewise() function and fit a piecewise linear regression.

from piecewise.regressor import piecewise

model = piecewise(t, v)

model if a FittedModel object. If you are at a shell, you can print the object to see the fitted segments domains and regression coefficients.

>>> model
FittedModel with segments:
* FittedSegment(start_t=0, end_t=5, coeffs=(-0.8576123780622642, 2.224791099812951))
* FittedSegment(start_t=5, end_t=9, coeffs=(10.975487672814133, -1.0722348284390741))

Alternatively, you can use the FittedModel's segments attribute to get at values.

>>> len(model.segments)
>>> model.segments[0].coeffs
(-0.8576123780622642, 2.224791099812951)

If you want to interpolate or extrapolate, you can use the FittedModel's predict() function.

>>> model.predict(t_new=[3.5, 100])
array([  6.92915647, -96.24799517])

To see a plot, instead of getting a FittedModel, use plot_data_with_regression().

from piecewise.plotter import plot_data_with_regression

plot_data_with_regression(t, v)