"Fossies" - the Fresh Open Source Software Archive  

Source code changes of the file "docs/_docs/quick_start.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.

quick_start.md  (prophet-1.0)	:	quick_start.md  (prophet-1.1)
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---		---
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## Python API		## Python API
Prophet follows the `sklearn` model API. We create an instance of the `Prophet` class and then call its `fit` and `predict` methods.		Prophet follows the `sklearn` model API. We create an instance of the `Prophet` class and then call its `fit` and `predict` methods.
The input to Prophet is always a dataframe with two columns: `ds` and `y`. The `ds` (datestamp) column should be of a format expected by Pandas, ideally YYYY-M M-DD for a date or YYYY-MM-DD HH:MM:SS for a timestamp. The `y` column must be n umeric, and represents the measurement we wish to forecast.		The input to Prophet is always a dataframe with two columns: `ds` and `y`. The `ds` (datestamp) column should be of a format expected by Pandas, ideally YYYY-M M-DD for a date or YYYY-MM-DD HH:MM:SS for a timestamp. The `y` column must be n umeric, and represents the measurement we wish to forecast.
As an example, let's look at a time series of the log daily page views for the W ikipedia page for [Peyton Manning](https://en.wikipedia.org/wiki/Peyton_Manning) . We scraped this data using the [Wikipediatrend](https://cran.r-project.org/pa ckage=wikipediatrend) package in R. Peyton Manning provides a nice example beca use it illustrates some of Prophet's features, like multiple seasonality, changi ng growth rates, and the ability to model special days (such as Manning's playof f and superbowl appearances). The CSV is available [here](https://github.com/fac ebook/prophet/blob/master/examples/example_wp_log_peyton_manning.csv).		As an example, let's look at a time series of the log daily page views for the W ikipedia page for [Peyton Manning](https://en.wikipedia.org/wiki/Peyton_Manning) . We scraped this data using the [Wikipediatrend](https://cran.r-project.org/pa ckage=wikipediatrend) package in R. Peyton Manning provides a nice example beca use it illustrates some of Prophet's features, like multiple seasonality, changi ng growth rates, and the ability to model special days (such as Manning's playof f and superbowl appearances). The CSV is available [here](https://github.com/fac ebook/prophet/blob/main/examples/example_wp_log_peyton_manning.csv).
First we'll import the data:		First we'll import the data:
```python		```python
# Python		# Python
import pandas as pd		import pandas as pd
from prophet import Prophet		from prophet import Prophet
```		```
```python		```python
# Python		# Python
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In R, we use the normal model fitting API. We provide a `prophet` function that performs fitting and returns a model object. You can then call `predict` and ` plot` on this model object.		In R, we use the normal model fitting API. We provide a `prophet` function that performs fitting and returns a model object. You can then call `predict` and ` plot` on this model object.
```R		```R
# R		# R
library(prophet)		library(prophet)
```		```
R[write to console]: Loading required package: Rcpp		R[write to console]: Loading required package: Rcpp
R[write to console]: Loading required package: rlang		R[write to console]: Loading required package: rlang
First we read in the data and create the outcome variable. As in the Python API, this is a dataframe with columns `ds` and `y`, containing the date and numeric value respectively. The ds column should be YYYY-MM-DD for a date, or YYYY-MM-DD HH:MM:SS for a timestamp. As above, we use here the log number of views to Peyt on Manning's Wikipedia page, available [here](https://github.com/facebook/prophe t/blob/master/examples/example_wp_log_peyton_manning.csv).		First we read in the data and create the outcome variable. As in the Python API, this is a dataframe with columns `ds` and `y`, containing the date and numeric value respectively. The ds column should be YYYY-MM-DD for a date, or YYYY-MM-DD HH:MM:SS for a timestamp. As above, we use here the log number of views to Peyt on Manning's Wikipedia page, available [here](https://github.com/facebook/prophe t/blob/main/examples/example_wp_log_peyton_manning.csv).
```R		```R
# R		# R
df <- read.csv('../examples/example_wp_log_peyton_manning.csv')		df <- read.csv('../examples/example_wp_log_peyton_manning.csv')
```		```
We call the `prophet` function to fit the model. The first argument is the hist orical dataframe. Additional arguments control how Prophet fits the data and ar e described in later pages of this documentation.		We call the `prophet` function to fit the model. The first argument is the hist orical dataframe. Additional arguments control how Prophet fits the data and ar e described in later pages of this documentation.
```R		```R
# R		# R
m <- prophet(df)		m <- prophet(df)
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