"Fossies" - the Fresh Open Source Software Archive  

Source code changes of the file "docs/_docs/uncertainty_intervals.md" between
prophet-1.1.tar.gz and prophet-1.1.1.tar.gz

About: Prophet is a tool for producing high quality forecasts for time series data that has multiple seasonality with linear or non-linear growth.

uncertainty_intervals.md  (prophet-1.1):uncertainty_intervals.md  (prophet-1.1.1)
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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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