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By default Prophet will only return uncertainty in the trend and observation noi se. To get uncertainty in seasonality, you must do full Bayesian sampling. This is done using the parameter `mcmc.samples` (which defaults to 0). We do this her e for the first six months of the Peyton Manning data from the Quickstart: | By default Prophet will only return uncertainty in the trend and observation noi se. To get uncertainty in seasonality, you must do full Bayesian sampling. This is done using the parameter `mcmc.samples` (which defaults to 0). We do this her e for the first six months of the Peyton Manning data from the Quickstart: | |||

```R | ```R | |||

# R | # R | |||

m <- prophet(df, mcmc.samples = 300) | m <- prophet(df, mcmc.samples = 300) | |||

forecast <- predict(m, future) | forecast <- predict(m, future) | |||

``` | ``` | |||

```python | ```python | |||

# Python | # Python | |||

m = Prophet(mcmc_samples=300) | m = Prophet(mcmc_samples=300) | |||

forecast = m.fit(df).predict(future) | forecast = m.fit(df, show_progress=False).predict(future) | |||

``` | ``` | |||

This replaces the typical MAP estimation with MCMC sampling, and can take much l onger depending on how many observations there are - expect several minutes inst ead of several seconds. If you do full sampling, then you will see the uncertain ty in seasonal components when you plot them: | This replaces the typical MAP estimation with MCMC sampling, and can take much l onger depending on how many observations there are - expect several minutes inst ead of several seconds. If you do full sampling, then you will see the uncertain ty in seasonal components when you plot them: | |||

```R | ```R | |||

# R | # R | |||

prophet_plot_components(m, forecast) | prophet_plot_components(m, forecast) | |||

``` | ``` | |||

```python | ```python | |||

# Python | # Python | |||

fig = m.plot_components(forecast) | fig = m.plot_components(forecast) | |||

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