# Modeling count time series (Negative Binomial VS Normal)

I’m new using PyMC3 and I’m reproducing the work of this blog to understand the underlying logic of Facebook’s Prophet algorithm.

For reference, I’m using the code at the end of this post. I’ve noticed that the data used in the example is a time series of Wikipedia page views, which is basically a count time series. They used the log version of it to model it using a Normal distribution as highlighted by this code:

pm.Normal('obs',
mu=y,
sd=sigma,
observed=df['y_scaled'])


So, I’ve three questions regarding this:

1. Why or when is better to use the log version of a count time series to model it with a normal distribution?

2. Given that we are working with counts, does it make sense to work with the negative binomial distribution? I actually went ahead and tried it, but I guess I’m doing something wrong because I get this error: ValueError: operands could not be broadcast together with shapes (500,2905) (500,). I’m using this to replace the corresponding lines:

def trend_model(m, t, n_changepoints=25, changepoints_prior_scale=0.1,
growth_prior_scale=0.5, changepoint_range=0.8):
"""
The piecewise linear trend with changepoint implementation in PyMC3.
:param m: (pm.Model)
:param t: (np.array) MinMax scaled time.
:param n_changepoints: (int) The number of changepoints to model.
:param changepoint_prior_scale: (flt/ None) The scale of the Laplace prior on the delta vector.
If None, a hierarchical prior is set.
:param growth_prior_scale: (flt) The standard deviation of the prior on the growth.
:param changepoint_range: (flt) Proportion of history in which trend changepoints will be estimated.
:return g, A, s: (tt.vector, np.array, tt.vector)
"""
s = np.linspace(0, changepoint_range * np.max(t), n_changepoints + 1)[1:]

# * 1 casts the boolean to integers
A = (t[:, None] > s) * 1

with m:
# initial growth
k = pm.Normal('k', 2, growth_prior_scale)

if changepoints_prior_scale is None:
changepoints_prior_scale = pm.Exponential('tau', 1.5)

# rate of change
delta = pm.Laplace('delta', 0, changepoints_prior_scale, shape=n_changepoints)
# offset
m = pm.Normal('m', 100, 50)
gamma = -s * delta

g = (m + det_dot(A, gamma)) * (k + det_dot(A, delta)) ** t
return g, A, s

# Generate a PyMC3 Model context
m = pm.Model()

with m:
y, A, s = trend_model(m, df['t'])

#sigma = pm.HalfCauchy('sigma', 0.5, testval=1)
alpha = pm.HalfCauchy('sigma', 0.5, testval=1)
pm.NegativeBinomial('obs',
mu=y,
alpha=alpha,
observed=df['y_original'])

3. In case I’ve a bunch of time series of page views from different sites, what’s the most convenient way to modify this to a hierarchical code to take advantage of the information of all the time series, instead of creating a single model for each one.

Any help / suggestion / comment on these questions is highly appreciated!

Full code below:

import numpy as np
import pymc3 as pm
import matplotlib.pyplot as plt
import pandas as pd

np.random.seed(25)
n_changepoints = 10
t = np.arange(1000)
s = np.sort(np.random.choice(t, n_changepoints, replace=False))

A = (t[:, None] > s) * 1
delta = np.random.normal(size=n_changepoints)
k = 1
m = 5

growth = (k + A @ delta) * t
gamma = -s * delta
offset = m + A @ gamma
trend = growth + offset

plt.figure(figsize=(16, 3 * 3))
n = 310
i = 0
for t, f in zip(['Linear Trend with Changepoints', 'Growth rate', 'Growth offset'],
[trend, growth, offset]):
i += 1
plt.subplot(n + i)
plt.title(t)
plt.yticks([])
plt.vlines(s, min(f), max(f), lw=0.8, linestyles='--')
plt.plot(f)
plt.show()

def det_dot(a, b):
"""
The theano dot product and NUTS sampler don't work with large matrices?

:param a: (np matrix)
:param b: (theano vector)
"""
return (a * b[None, :]).sum(axis=-1)

# Make sure we work with datetime types
df['ds'] = pd.to_datetime(df['ds'])
# Scale the data
df['y_scaled'] = df['y'] / df['y'].max()
df['t'] = (df['ds'] - df['ds'].min()) / (df['ds'].max() - df['ds'].min())
plt.show()

def trend_model(m, t, n_changepoints=25, changepoints_prior_scale=0.1,
growth_prior_scale=5, changepoint_range=0.8):
"""
The piecewise linear trend with changepoint implementation in PyMC3.
:param m: (pm.Model)
:param t: (np.array) MinMax scaled time.
:param n_changepoints: (int) The number of changepoints to model.
:param changepoint_prior_scale: (flt/ None) The scale of the Laplace prior on the delta vector.
If None, a hierarchical prior is set.
:param growth_prior_scale: (flt) The standard deviation of the prior on the growth.
:param changepoint_range: (flt) Proportion of history in which trend changepoints will be estimated.
:return g, A, s: (tt.vector, np.array, tt.vector)
"""
s = np.linspace(0, changepoint_range * np.max(t), n_changepoints + 1)[1:]

# * 1 casts the boolean to integers
A = (t[:, None] > s) * 1

with m:
# initial growth
k = pm.Normal('k', 0, growth_prior_scale)

if changepoints_prior_scale is None:
changepoints_prior_scale = pm.Exponential('tau', 1.5)

# rate of change
delta = pm.Laplace('delta', 0, changepoints_prior_scale, shape=n_changepoints)
# offset
m = pm.Normal('m', 0, 5)
gamma = -s * delta

g = (k + det_dot(A, delta)) * t + (m + det_dot(A, gamma))
return g, A, s

# Generate a PyMC3 Model context
m = pm.Model()

with m:
y, A, s = trend_model(m, df['t'])

sigma = pm.HalfCauchy('sigma', 0.5, testval=1)
pm.Normal('obs',
mu=y,
sd=sigma,
observed=df['y_scaled'])

def sanity_check(m, df):
"""
:param m: (pm.Model)
:param df: (pd.DataFrame)
"""
# Sample from the prior and check of the model is well defined.
y = pm.sample_prior_predictive(model=m, vars=['obs'])['obs']
plt.figure(figsize=(16, 6))
plt.plot(y.mean(0), label='mean prior')
plt.fill_between(np.arange(y.shape[1]), -y.std(0), y.std(0), alpha=0.25, label='standard deviation')
plt.plot(df['y_scaled'], label='true value')
plt.legend()
plt.show()

# And run the sanity check
sanity_check(m, df)

# Find a point estimate of the models parameters
with m:
aprox = pm.find_MAP()

# Determine g, based on the parameters
def det_trend(k, m, delta, t, s, A):
return (k + np.dot(A, delta)) * t + (m + np.dot(A, (-s * delta)))

# run function and rescale to original scale
g = det_trend(aprox['k'], aprox['m'], aprox['delta'], df['t'], s, A) * df['y'].max()

plt.figure(figsize=(16, 6))
plt.title('$g(t)$')
plt.plot(g)
plt.scatter(np.arange(df.shape[0]), df.y, s=0.5, color='black')
plt.show()

np.random.seed(6)
def fourier_series(t, p=365.25, n=10):
# 2 pi n / p
x = 2 * np.pi * np.arange(1, n + 1) / p
# 2 pi n / p * t
x = x * t[:, None]
x = np.concatenate((np.cos(x), np.sin(x)), axis=1)
return x

def seasonality_model(m, df, period='yearly', seasonality_prior_scale=10):
if period == 'yearly':
n = 10
# rescale the period, as t is also scaled
p = 365.25 / (df['ds'].max() - df['ds'].min()).days
else:  # weekly
n = 3
# rescale the period, as t is also scaled
p = 7 / (df['ds'].max() - df['ds'].min()).days
x = fourier_series(df['t'], p, n)
with m:
beta = pm.Normal(f'beta_{period}', mu=0, sd=seasonality_prior_scale, shape=2 * n)
return x, beta

m = pm.Model()

with m:
# changepoints_prior_scale is None, so the exponential distribution
# will be used as prior on \tau.
y, A, s = trend_model(m, df['t'], changepoints_prior_scale=None)
x_yearly, beta_yearly = seasonality_model(m, df, 'yearly')
x_weekly, beta_weekly = seasonality_model(m, df, 'weekly')

y += det_dot(x_yearly, beta_yearly) + det_dot(x_weekly, beta_weekly)

sigma = pm.HalfCauchy('sigma', 0.5, testval=1)
obs = pm.Normal('obs',
mu=y,
sd=sigma,
observed=df['y_scaled'])

with m:
trace = pm.sample(500)
pm.traceplot(trace)

def det_seasonality_posterior(beta, x):
return np.dot(x, beta.T)

p = 0.025
# vector distributions
beta_yearly = trace['beta_yearly']
beta_weekly = trace['beta_weekly']
delta = trace['delta']

# scalar distributions
k = trace['k']
m = trace['m']

# determine the posterior by evaulating all the values in the trace.
trend_posterior = ((k + np.dot(A, delta.T)) * df['t'][:, None] + m + np.dot(A, (-s * delta).T)) * df['y'].max()

yearly_posterior = det_seasonality_posterior(beta_yearly, x_yearly) * df['y'].max()
weekly_posterior = det_seasonality_posterior(beta_weekly, x_weekly) * df['y'].max()

date = df['ds'].dt.to_pydatetime()
sunday = np.argmax(df['ds'].dt.dayofweek)
weekdays = ['sunday', 'monday', 'tuesday', 'wednesday', 'thursday', 'friday', 'saturday']
idx_year = np.argmax(df['ds'].dt.dayofyear)

plt.figure(figsize=(16, 3*6))
b = 411
plt.subplot(b)
plt.title('total')
plt.plot(date,
(trend_posterior + yearly_posterior + weekly_posterior).mean(1), lw=0.5)
plt.scatter(date, df['y'], s=0.5, color='black')

plt.subplot(b + 1)
plt.title('trend')
plt.plot(date, trend_posterior.mean(1))
quant = np.quantile(trend_posterior, [p, 1 - p], axis=1)
plt.fill_between(date, quant[0, :], quant[1, :], alpha=0.25)

plt.subplot(b + 2)
plt.title('yearly')
plt.plot(date[idx_year: idx_year + 365], yearly_posterior.mean(1)[idx_year: idx_year + 365])
quant = np.quantile(yearly_posterior, [p, 1 - p], axis=1)
plt.fill_between(date[idx_year: idx_year + 365],
quant[0, idx_year: idx_year + 365], quant[1, idx_year: idx_year + 365], alpha=0.25)

plt.subplot(b + 3)
plt.title('weekly')
plt.plot(weekdays, weekly_posterior.mean(1)[sunday: sunday + 7])
quant = np.quantile(weekly_posterior, [p, 1 - p], axis=1)
plt.fill_between(weekdays, quant[0, sunday: sunday + 7],
quant[1, sunday: sunday + 7], alpha=0.25)

1. In general taking the logs before fitting the regression is beneficial to fitting it, if you observe heteroskedasticity (i.e. errors funnelling out). Not too sure why it was really needed with this particular example from facebook honestly as I don’t expect this particular count data to behave crazily.
2. Prophet basically models the mean of the sum of the Bx (where B are have either normal or laplacian priors) with a normal distribution. In most cases Normals provide good approximation even for count/discrete distributions in GLM and behave much better with samplers (as they are defined over continuous spaces, rather than on discrete ones).

The reason why the model is normal in the original prophet is because you have something like:

Normal(mu: seasonality TS generated from Fourier timeseries (continuous) * B1<normal/laplacian> + trend * B2<normal> + additional regressors * B3<normal/laplacian/other in pmprophet> + intercept<normal>, sigma: ..., observed = some_data)

Which usually gives good fits even with observed count data. Prophet doesn’t allow for extensions but you can indeed substitute the normal with something else in pmprophet;

m = PMProphet(df, ..., name='model')

with m.model:
pm.Normal( # replace me with whatever you like, just make sure the sampler likes me
"y_%s" % m.name,
mu=m.y, # the sum of the various regressors (trend, seasonality, intercept.. is stored in m.y)
alpha=m.priors["sigma"],
observed=(...),
)
pm.Deterministic("y_hat_%s" % m.name, m.y)

m.fit(
draws=10 ** 4,
method=Sampler.NUTS,
finalize=False # this will prevent the model from using the default normal mean fit (see https://github.com/luke14free/pm-prophet/blob/master/pmprophet/model.py#L522)
)

1. I am unfortunately a bit too busy to go through your code, but you can provide custom priors to pmprophet, and therefore even hierarchical ones. This will allow you to fit multiple models at the same time (having for example an hierarchical prior on seasonality, additional regressors like CTR or whatever you want). Have a look m.priors.

ciao!

4 Likes

Thanks so much for your reply, Luca! This is really helpful and a good start to dive deep.

Molto grazie!