"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)
skipping to change at line 143 skipping to change at line 143
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
![png](/prophet/static/non-daily_data_files/non-daily_data_19_1.png) ![png](/prophet/static/non-daily_data_files/non-daily_data_19_1.png)
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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