pm-prophet

Pymc3-based universal time series prediction and decomposition library (inspired by Facebook Prophet). However, while Faceook prophet is a well-defined model, pm-prophet allows for total flexibility in the choice of priors and thus is potentially suited for a wider class of estimation problems.

⚠️ Only supports Python 3

Installing pm-prophet
Key features
Differences with [Prophet]
Peyton Manning example
Custom Priors

Installing pm-prophet

PM-Prophet installation is straightforward using pip: pip install pmprophet

Note that the key dependency of pm-prophet is PyMc3 a library that depends on Theano.

Key Features

Nowcasting & Forecasting
Intercept, growth
Regressors
Holidays
Additive & multiplicative seasonality
Fitting and plotting
Custom choice of priors (not in Facebook's prophet original model)
Changepoints in growth
Automatic changepoint location detection (not in Facebook's prophet original model)
Fitting with NUTS/AVDI/Metropolis

Experimental warning ⚠️

Note that automatic changepoint detection is experimental

Differences with Prophet:

Saturating growth is not implemented
Uncertainty estimation is different
All components (including seasonality) need to be explicitly added to the model
By design pm-prophet places a big emphasis on posteriors and uncertainty estimates, and therefore it won't use MAP for it's estimates.
While Faceook prophet is a well-defined fixed model, pm-prophet allows for total flexibility in the choice of priors and thus is potentially suited for a wider class of estimation problems

Peyton Manning example

Predicting the Peyton Manning timeseries:

import pandas as pd
from pmprophet.model import PMProphet, Sampler

df = pd.read_csv("examples/example_wp_log_peyton_manning.csv")
df = df.head(180)

# Fit both growth and intercept
m = PMProphet(df, growth=True, intercept=True, n_changepoints=25, changepoints_prior_scale=.01, name='model')

# Add monthly seasonality (order: 3)
m.add_seasonality(seasonality=30, fourier_order=3)

# Add weekly seasonality (order: 3)
m.add_seasonality(seasonality=7, fourier_order=3)

# Fit the model (using NUTS)
m.fit(method=Sampler.NUTS)

ddf = m.predict(60, alpha=0.2, include_history=True, plot=True)
m.plot_components(
    intercept=False,
)

Custom Priors

One of the main reason why PMProphet was built is to allow custom priors for the modeling.

The default priors are:

Variable	Prior	Parameters
`regressors`	Laplace	loc:0, scale:2.5
`holidays`	Laplace	loc:0, scale:2.5
`seasonality`	Laplace	loc:0, scale:0.05
`growth`	Laplace	loc:0, scale:10
`changepoints`	Laplace	loc:0, scale:2.5
`intercept`	Normal	loc:`y.mean`, scale: `2 * y.std`
`sigma`	Half Cauchy	tau:10

But you can change model priors by inspecting and modifying the distributions stored in

m.priors

which is a dictionary of {prior: pymc3-distribution}.

In the example below we will model an additive time-series by imposing a "positive coefficients" constraint by using an Exponential distribution instead of a Laplacian distribution for the regressors.

import pandas as pd
import numpy as np
import pymc3 as pm
from pmprophet.model import PMProphet, Sampler

n_timesteps = 100
n_regressors = 20

regressors = np.random.normal(size=(n_timesteps, n_regressors))
coeffs = np.random.exponential(size=n_regressors) + np.random.normal(size=n_regressors)
# Note that min(coeffs) could be negative due to the white noise

regressors_names = [str(i) for i in range(n_regressors)]

df = pd.DataFrame()
df['y'] = np.dot(regressors, coeffs)
df['ds'] = pd.date_range('2017-01-01', periods=n_timesteps)
for idx, regressor in enumerate(regressors_names):
    df[regressor] = regressors[:, idx]

m = PMProphet(df, growth=False, intercept=False, n_changepoints=0, name='model')

with m.model:
    # Remember to suffix _<model-name> to the custom priors
    m.priors['regressors'] = pm.Exponential('regressors_%s' % m.name, 1, shape=n_regressors)

for regressor in regressors_names:
    m.add_regressor(regressor)

m.fit(
    draws=10 ** 4,
    method=Sampler.NUTS,
)
m.plot_components()

Automatic changepoint detection (⚠️experimental)

Pm-prophet is equipped with a non-parametric truncated Dirichlet Process allowing it to automatically detect changepoints in the trend.

To enable it simply initialize the model with auto_changepoints=True as follows:

from pmprophet.model import PMProphet, Sampler
import pandas as pd

df = pd.read_csv("examples/example_wp_log_peyton_manning.csv")
df = df.head(180)
m = PMProphet(df, auto_changepoints=True, growth=True, intercept=True, name='model')
m.fit(method=Sampler.METROPOLIS, draws=2000)
m.predict(60, alpha=0.2, include_history=True, plot=True)
m.plot_components(
    intercept=False,
)

Where n_changepoints is interpreted as the truncation point for the Dirichlet Process.

Pm-prophet will then decide which changepoint values make sense and add a custom weight to them. A call to plot_components() will reveal the changepoint map:

A few caveats exist:

It's slow to fit since it's a non-parametric model
For best results use NUTS as method
It will likely require more than the default number of draws to converge

madHatter106/pm-prophet