Time Series Contributions Welcome?

junpenglao · August 28, 2017, 10:15am

That’s great @cshenton! We have some plans to extend and refine time-series distribution (see github issus).

You should have a look at the random method in pm.disribution for some example. In general we import random method from scipy.stats to generate samples:

github.com

pymc-devs/pymc3/blob/d4749cd0865927ff0430800272c39855acb9bc96/pymc3/distributions/continuous.py#L288-L293


def random(self, point=None, size=None, repeat=None):
    mu, tau, _ = draw_values([self.mu, self.tau, self.sd],
                             point=point)
    return generate_samples(stats.norm.rvs, loc=mu, scale=tau**-0.5,
                            dist_shape=self.shape,
                            size=size)

In case there is no correspondent scipy object, we implement a random method (usually hidden using _random):

github.com

pymc-devs/pymc3/blob/d4749cd0865927ff0430800272c39855acb9bc96/pymc3/distributions/continuous.py#L495-L510


def _random(self, mu, lam, alpha, size=None):
    v = np.random.normal(size=size)**2
    value = (mu + (mu**2) * v / (2. * lam) - mu / (2. * lam)
             * np.sqrt(4. * mu * lam * v + (mu * v)**2))
    z = np.random.uniform(size=size)
    i = np.floor(z - mu / (mu + value)) * 2 + 1
    value = (value**-i) * (mu**(i + 1))
    return value + alpha


def random(self, point=None, size=None, repeat=None):
    mu, lam, alpha = draw_values([self.mu, self.lam, self.alpha],
                                 point=point)
    return generate_samples(self._random,
                            mu, lam, alpha,
                            dist_shape=self.shape,
                            size=size)

PR welcome!

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Time Series Contributions Welcome?

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