"Fossies" - the Fresh Open Source Software Archive  

Source code changes of the file "docs/_docs/non-daily_data.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.

non-daily_data.md  (prophet-0.7)	:	non-daily_data.md  (prophet-1.0)
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m <- prophet(df, seasonality.mode = 'multiplicative', mcmc.samples = 300)		m <- prophet(df, seasonality.mode = 'multiplicative', mcmc.samples = 300)
fcst <- predict(m, future)		fcst <- predict(m, future)
prophet_plot_components(m, fcst)		prophet_plot_components(m, fcst)
```		```
```python		```python
# Python		# Python
m = Prophet(seasonality_mode='multiplicative', mcmc_samples=300).fit(df)		m = Prophet(seasonality_mode='multiplicative', mcmc_samples=300).fit(df)
fcst = m.predict(future)		fcst = m.predict(future)
fig = m.plot_components(fcst)		fig = m.plot_components(fcst)
```		```
WARNING:pystan:403 of 600 iterations saturated the maximum tree depth of 10 (67.2 %)		WARNING:pystan:481 of 600 iterations saturated the maximum tree depth of 10 (80.2 %)
WARNING:pystan:Run again with max_treedepth larger than 10 to avoid saturati on		WARNING:pystan:Run again with max_treedepth larger than 10 to avoid saturati on
		
The seasonality has low uncertainty at the start of each month where there are d ata points, but has very high posterior variance in between. When fitting Prophe t to monthly data, only make monthly forecasts, which can be done by passing the frequency into `make_future_dataframe`:		The seasonality has low uncertainty at the start of each month where there are d ata points, but has very high posterior variance in between. When fitting Prophe t to monthly data, only make monthly forecasts, which can be done by passing the frequency into `make_future_dataframe`:
```R		```R
# R		# R
future <- make_future_dataframe(m, periods = 120, freq = 'month')		future <- make_future_dataframe(m, periods = 120, freq = 'month')
fcst <- predict(m, future)		fcst <- predict(m, future)
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