"Fossies" - the Fresh Open Source Software Archive  

Source code changes of the file "notebooks/quick_start.ipynb" 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.ipynb  (prophet-1.0):quick_start.ipynb  (prophet-1.1)
skipping to change at line 34 skipping to change at line 34
"\n", "\n",
"Prophet follows the `sklearn` model API. We create an instance of the `Pro phet` class and then call its `fit` and `predict` methods. " "Prophet follows the `sklearn` model API. We create an instance of the `Pro phet` class and then call its `fit` and `predict` methods. "
] ]
}, },
{ {
"cell_type": "markdown", "cell_type": "markdown",
"metadata": {}, "metadata": {},
"source": [ "source": [
"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 Y YYY-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", "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 Y YYY-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", "\n",
"As an example, let's look at a time series of the log daily page views for the Wikipedia page for [Peyton Manning](https://en.wikipedia.org/wiki/Peyton_Man ning). We scraped this data using the [Wikipediatrend](https://cran.r-project.o rg/package=wikipediatrend) package in R. Peyton Manning provides a nice example because it illustrates some of Prophet's features, like multiple seasonality, c hanging growth rates, and the ability to model special days (such as Manning's p layoff and superbowl appearances). The CSV is available [here](https://github.co m/facebook/prophet/blob/master/examples/example_wp_log_peyton_manning.csv).\n", "As an example, let's look at a time series of the log daily page views for the Wikipedia page for [Peyton Manning](https://en.wikipedia.org/wiki/Peyton_Man ning). We scraped this data using the [Wikipediatrend](https://cran.r-project.o rg/package=wikipediatrend) package in R. Peyton Manning provides a nice example because it illustrates some of Prophet's features, like multiple seasonality, c hanging growth rates, and the ability to model special days (such as Manning's p layoff and superbowl appearances). The CSV is available [here](https://github.co m/facebook/prophet/blob/main/examples/example_wp_log_peyton_manning.csv).\n",
"\n", "\n",
"First we'll import the data:" "First we'll import the data:"
] ]
}, },
{ {
"cell_type": "code", "cell_type": "code",
"execution_count": 2, "execution_count": 2,
"metadata": {}, "metadata": {},
"outputs": [], "outputs": [],
"source": [ "source": [
skipping to change at line 358 skipping to change at line 358
"You can plot the forecast by calling the `Prophet.plot` method and passing in your forecast dataframe." "You can plot the forecast by calling the `Prophet.plot` method and passing in your forecast dataframe."
] ]
}, },
{ {
"cell_type": "code", "cell_type": "code",
"execution_count": 7, "execution_count": 7,
"metadata": {}, "metadata": {},
"outputs": [ "outputs": [
{ {
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"text/plain": [ "text/plain": [
"<Figure size 720x432 with 1 Axes>" "<Figure size 720x432 with 1 Axes>"
] ]
}, },
"metadata": {}, "metadata": {},
"output_type": "display_data" "output_type": "display_data"
} }
], ],
"source": [ "source": [
"fig1 = m.plot(forecast)" "fig1 = m.plot(forecast)"
skipping to change at line 385 skipping to change at line 385
"If you want to see the forecast components, you can use the `Prophet.plot_c omponents` method. By default you'll see the trend, yearly seasonality, and wee kly seasonality of the time series. If you include holidays, you'll see those h ere, too." "If you want to see the forecast components, you can use the `Prophet.plot_c omponents` method. By default you'll see the trend, yearly seasonality, and wee kly seasonality of the time series. If you include holidays, you'll see those h ere, too."
] ]
}, },
{ {
"cell_type": "code", "cell_type": "code",
"execution_count": 8, "execution_count": 8,
"metadata": {}, "metadata": {},
"outputs": [ "outputs": [
{ {
"data": { "data": {
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"text/plain": [ "text/plain": [
"<Figure size 648x648 with 3 Axes>" "<Figure size 648x648 with 3 Axes>"
] ]
}, },
"metadata": {}, "metadata": {},
"output_type": "display_data" "output_type": "display_data"
} }
], ],
"source": [ "source": [
"fig2 = m.plot_components(forecast)" "fig2 = m.plot_components(forecast)"
skipping to change at line 466 skipping to change at line 466
], ],
"source": [ "source": [
"%%R\n", "%%R\n",
"library(prophet)" "library(prophet)"
] ]
}, },
{ {
"cell_type": "markdown", "cell_type": "markdown",
"metadata": {}, "metadata": {},
"source": [ "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 num eric 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 Peyton Manning's Wikipedia page, available [here](https://github.com/facebook/p rophet/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 num eric 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 Peyton Manning's Wikipedia page, available [here](https://github.com/facebook/p rophet/blob/main/examples/example_wp_log_peyton_manning.csv)."
] ]
}, },
{ {
"cell_type": "code", "cell_type": "code",
"execution_count": 3, "execution_count": 3,
"metadata": {}, "metadata": {},
"outputs": [], "outputs": [],
"source": [ "source": [
"%%R\n", "%%R\n",
"df <- read.csv('../examples/example_wp_log_peyton_manning.csv')" "df <- read.csv('../examples/example_wp_log_peyton_manning.csv')"
skipping to change at line 585 skipping to change at line 585
"You can use the generic `plot` function to plot the forecast, by passing in the model and the forecast dataframe." "You can use the generic `plot` function to plot the forecast, by passing in the model and the forecast dataframe."
] ]
}, },
{ {
"cell_type": "code", "cell_type": "code",
"execution_count": 7, "execution_count": 7,
"metadata": {}, "metadata": {},
"outputs": [ "outputs": [
{ {
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}, },
"metadata": {}, "metadata": {},
"output_type": "display_data" "output_type": "display_data"
} }
], ],
"source": [ "source": [
"%%R -w 10 -h 6 -u in\n", "%%R -w 10 -h 6 -u in\n",
"plot(m, forecast)" "plot(m, forecast)"
] ]
}, },
skipping to change at line 610 skipping to change at line 610
"You can use the `prophet_plot_components` function to see the forecast brok en down into trend, weekly seasonality, and yearly seasonality." "You can use the `prophet_plot_components` function to see the forecast brok en down into trend, weekly seasonality, and yearly seasonality."
] ]
}, },
{ {
"cell_type": "code", "cell_type": "code",
"execution_count": 8, "execution_count": 8,
"metadata": {}, "metadata": {},
"outputs": [ "outputs": [
{ {
"data": { "data": {
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}, },
"metadata": {}, "metadata": {},
"output_type": "display_data" "output_type": "display_data"
} }
], ],
"source": [ "source": [
"%%R -w 9 -h 9 -u in\n", "%%R -w 9 -h 9 -u in\n",
"prophet_plot_components(m, forecast)" "prophet_plot_components(m, forecast)"
] ]
}, },
 End of changes. 6 change blocks. 
6 lines changed or deleted 6 lines changed or added

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