6.11.2.3.8. statsmodels.sandbox.tsa.diffusion.OUprocess¶
-
class
statsmodels.sandbox.tsa.diffusion.OUprocess(xzero, mu, lambd, sigma)[source]¶ Ornstein-Uhlenbeck
- :math::
- dx_t&=lambda(mu - x_t)dt+sigma dW_t
mean reverting process
TODO: move exact higher up in class hierarchy
6.11.2.3.8.1. Methods¶
__init__(xzero, mu, lambd, sigma) |
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exact(xzero, t, normrvs) |
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exactdist(xzero, t) |
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exactprocess(xzero, nobs[, ddt, nrepl]) |
ddt : discrete delta t |
expectedsim(func[, nobs, T, dt, nrepl]) |
get expectation of a function of a Wiener Process by simulation |
fitls(data, dt) |
assumes data is 1d, univariate time series |
sim([nobs, T, dt, nrepl]) |
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simEM([xzero, nobs, T, dt, nrepl, Tratio]) |
from Higham 2001 |
simulateW([nobs, T, dt, nrepl]) |
generate sample of Wiener Process |