"Fossies" - the Fresh Open Source Software Archive  

Source code changes of the file "docs/_docs/uncertainty_intervals.md" between
prophet-0.7.tar.gz and prophet-1.0.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-0.7):uncertainty_intervals.md  (prophet-1.0)
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```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)
``` ```
![png](/prophet/static/uncertainty_intervals_files/uncertainty_intervals_10_0.pn g) ![png](/prophet/static/uncertainty_intervals_files/uncertainty_intervals_11_0.pn g)
You can access the raw posterior predictive samples in Python using the method ` m.predictive_samples(future)`, or in R using the function `predictive_samples(m, future)`. You can access the raw posterior predictive samples in Python using the method ` m.predictive_samples(future)`, or in R using the function `predictive_samples(m, future)`.
There are upstream issues in PyStan for Windows which make MCMC sampling extreme ly slow. The best choice for MCMC sampling in Windows is to use R, or Python in a Linux VM. There are upstream issues in PyStan for Windows which make MCMC sampling extreme ly slow. The best choice for MCMC sampling in Windows is to use R, or Python in a Linux VM.
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