"Fossies" - the Fresh Open Source Software Archive  

Source code changes of the file "docs/_docs/non-daily_data.md" between
prophet-1.0.tar.gz and prophet-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.

non-daily_data.md  (prophet-1.0)	:	non-daily_data.md  (prophet-1.1)
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id: data-with-regular-gaps		id: data-with-regular-gaps
- title: Monthly data		- title: Monthly data
id: monthly-data		id: monthly-data
- title: Holidays with aggregated data		- title: Holidays with aggregated data
id: holidays-with-aggregated-data		id: holidays-with-aggregated-data
---		---
<a id="sub-daily-data"> </a>		<a id="sub-daily-data"> </a>
## Sub-daily data		## Sub-daily data
Prophet can make forecasts for time series with sub-daily observations by passin g in a dataframe with timestamps in the `ds` column. The format of the timestamp s should be YYYY-MM-DD HH:MM:SS - see the example csv [here](https://github.com/ facebook/prophet/blob/master/examples/example_yosemite_temps.csv). When sub-dail y data are used, daily seasonality will automatically be fit. Here we fit Prophe t to data with 5-minute resolution (daily temperatures at Yosemite):		Prophet can make forecasts for time series with sub-daily observations by passin g in a dataframe with timestamps in the `ds` column. The format of the timestamp s should be YYYY-MM-DD HH:MM:SS - see the example csv [here](https://github.com/ facebook/prophet/blob/main/examples/example_yosemite_temps.csv). When sub-daily data are used, daily seasonality will automatically be fit. Here we fit Prophet to data with 5-minute resolution (daily temperatures at Yosemite):
```R		```R
# R		# R
df <- read.csv('../examples/example_yosemite_temps.csv')		df <- read.csv('../examples/example_yosemite_temps.csv')
m <- prophet(df, changepoint.prior.scale=0.01)		m <- prophet(df, changepoint.prior.scale=0.01)
future <- make_future_dataframe(m, periods = 300, freq = 60 * 60)		future <- make_future_dataframe(m, periods = 300, freq = 60 * 60)
fcst <- predict(m, future)		fcst <- predict(m, future)
plot(m, fcst)		plot(m, fcst)
```		```
```python		```python
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