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Specify ds format in documentation

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Ben Letham 2018-05-29 21:55:26 -07:00
parent cdc313584e
commit 3571a90558
2 changed files with 3 additions and 3 deletions
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2
notebooks/non-daily_data.ipynb
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@ -37,7 +37,7 @@
"source": [
"## Sub-daily data\n",
"\n",
"Prophet can make forecasts for time series with sub-daily observations by passing in a dataframe with timestamps in the `ds` column. 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):"
"Prophet can make forecasts for time series with sub-daily observations by passing in a dataframe with timestamps in the `ds` column. The format of the timestamps 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-daily data are used, daily seasonality will automatically be fit. Here we fit Prophet to data with 5-minute resolution (daily temperatures at Yosemite):"
]
},
{

4
notebooks/quick_start.ipynb
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@ -29,7 +29,7 @@
"cell_type": "markdown",
"metadata": {},
"source": [
"The input to Prophet is always a dataframe with two columns: `ds` and `y`. The `ds` (datestamp) column must contain a date or datetime (either is fine). The `y` column must be numeric, and represents the measurement we wish to forecast.\n",
"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-MM-DD for a date or YYYY-MM-DD HH:MM:SS for a timestamp. The `y` column must be numeric, and represents the measurement we wish to forecast.\n",
"\n",
"As an example, let's look at a time series of daily page views for the Wikipedia page for [Peyton Manning](https://en.wikipedia.org/wiki/Peyton_Manning). We scraped this data using the [Wikipediatrend](https://cran.r-project.org/web/packages/wikipediatrend/vignettes/using-wikipediatrend.html) package in R. Peyton Manning provides a nice example because it illustrates some of Prophet's features, like multiple seasonality, changing growth rates, and the ability to model special days (such as Manning's playoff and superbowl appearances). The CSV is available [here](https://github.com/facebook/prophet/blob/master/examples/example_wp_peyton_manning.csv).\n",
"\n",
@ -409,7 +409,7 @@
"cell_type": "markdown",
"metadata": {},
"source": [
"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. As above, we use here the log number of views to Petyon Manning's Wikipedia page, available [here](https://github.com/facebook/prophet/blob/master/examples/example_wp_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 Petyon Manning's Wikipedia page, available [here](https://github.com/facebook/prophet/blob/master/examples/example_wp_peyton_manning.csv)."
]
},
{