uncertainty_intervals.ipynb (prophet-1.1) | : | uncertainty_intervals.ipynb (prophet-1.1.1) | ||
---|---|---|---|---|
{ | { | |||
"cells": [ | "cells": [ | |||
{ | { | |||
"cell_type": "code", | "cell_type": "code", | |||
"execution_count": 1, | "execution_count": 5, | |||
"metadata": { | "metadata": { | |||
"block_hidden": true | "block_hidden": true | |||
}, | }, | |||
"outputs": [], | "outputs": [ | |||
{ | ||||
"name": "stdout", | ||||
"output_type": "stream", | ||||
"text": [ | ||||
"The rpy2.ipython extension is already loaded. To reload it, use:\n", | ||||
" %reload_ext rpy2.ipython\n" | ||||
] | ||||
} | ||||
], | ||||
"source": [ | "source": [ | |||
"%load_ext rpy2.ipython\n", | "%load_ext rpy2.ipython\n", | |||
"%matplotlib inline\n", | "%matplotlib inline\n", | |||
"\n", | ||||
"from prophet import Prophet\n", | "from prophet import Prophet\n", | |||
"import pandas as pd\n", | ||||
"from matplotlib import pyplot as plt\n", | "from matplotlib import pyplot as plt\n", | |||
"import pandas as pd\n", | ||||
"import numpy as np\n", | "import numpy as np\n", | |||
"import logging\n", | "import logging\n", | |||
"logging.getLogger('prophet').setLevel(logging.ERROR)\n", | ||||
"import warnings\n", | "import warnings\n", | |||
"warnings.filterwarnings(\"ignore\")" | "\n", | |||
"logging.getLogger('prophet').setLevel(logging.ERROR)\n", | ||||
"logging.getLogger('cmdstanpy').setLevel(logging.ERROR)\n", | ||||
"warnings.filterwarnings('ignore')" | ||||
] | ] | |||
}, | }, | |||
{ | { | |||
"cell_type": "code", | "cell_type": "code", | |||
"execution_count": 2, | "execution_count": 6, | |||
"metadata": { | "metadata": { | |||
"block_hidden": true | "block_hidden": true | |||
}, | }, | |||
"outputs": [ | "outputs": [], | |||
{ | ||||
"name": "stderr", | ||||
"output_type": "stream", | ||||
"text": [ | ||||
"INFO:numexpr.utils:NumExpr defaulting to 8 threads.\n" | ||||
] | ||||
} | ||||
], | ||||
"source": [ | "source": [ | |||
"df = pd.read_csv('../examples/example_wp_log_peyton_manning.csv')\n", | "df = pd.read_csv('https://raw.githubusercontent.com/facebook/prophet/main/e xamples/example_wp_log_peyton_manning.csv')\n", | |||
"df = df.loc[:180,] # Limit to first six months\n", | "df = df.loc[:180,] # Limit to first six months\n", | |||
"m = Prophet()\n", | "m = Prophet()\n", | |||
"m.fit(df)\n", | "m.fit(df)\n", | |||
"future = m.make_future_dataframe(periods=60)" | "future = m.make_future_dataframe(periods=60)" | |||
] | ] | |||
}, | }, | |||
{ | { | |||
"cell_type": "code", | "cell_type": "code", | |||
"execution_count": 2, | "execution_count": 2, | |||
"metadata": { | "metadata": { | |||
skipping to change at line 71 | skipping to change at line 75 | |||
"R[write to console]: Disabling yearly seasonality. Run prophet with yearl y.seasonality=TRUE to override this.\n", | "R[write to console]: Disabling yearly seasonality. Run prophet with yearl y.seasonality=TRUE to override this.\n", | |||
"\n", | "\n", | |||
"R[write to console]: Disabling daily seasonality. Run prophet with daily. seasonality=TRUE to override this.\n", | "R[write to console]: Disabling daily seasonality. Run prophet with daily. seasonality=TRUE to override this.\n", | |||
"\n" | "\n" | |||
] | ] | |||
} | } | |||
], | ], | |||
"source": [ | "source": [ | |||
"%%R\n", | "%%R\n", | |||
"library(prophet)\n", | "library(prophet)\n", | |||
"df <- read.csv('../examples/example_wp_log_peyton_manning.csv')\n", | "df <- read.csv('https://raw.githubusercontent.com/facebook/prophet/main/exa mples/example_wp_log_peyton_manning.csv')\n", | |||
"df <- df[1:180,]\n", | "df <- df[1:180,]\n", | |||
"m <- prophet(df)\n", | "m <- prophet(df)\n", | |||
"future <- make_future_dataframe(m, periods=60)" | "future <- make_future_dataframe(m, periods=60)" | |||
] | ] | |||
}, | }, | |||
{ | { | |||
"cell_type": "markdown", | "cell_type": "markdown", | |||
"metadata": {}, | "metadata": {}, | |||
"source": [ | "source": [ | |||
"By default Prophet will return uncertainty intervals for the forecast `yhat `. There are several important assumptions behind these uncertainty intervals.\n ", | "By default Prophet will return uncertainty intervals for the forecast `yhat `. There are several important assumptions behind these uncertainty intervals.\n ", | |||
skipping to change at line 268 | skipping to change at line 272 | |||
} | } | |||
], | ], | |||
"source": [ | "source": [ | |||
"%%R\n", | "%%R\n", | |||
"m <- prophet(df, mcmc.samples = 300)\n", | "m <- prophet(df, mcmc.samples = 300)\n", | |||
"forecast <- predict(m, future)" | "forecast <- predict(m, future)" | |||
] | ] | |||
}, | }, | |||
{ | { | |||
"cell_type": "code", | "cell_type": "code", | |||
"execution_count": 4, | "execution_count": 7, | |||
"metadata": {}, | "metadata": {}, | |||
"outputs": [], | "outputs": [], | |||
"source": [ | "source": [ | |||
"m = Prophet(mcmc_samples=300)\n", | "m = Prophet(mcmc_samples=300)\n", | |||
"forecast = m.fit(df).predict(future)" | "forecast = m.fit(df, show_progress=False).predict(future)" | |||
] | ] | |||
}, | }, | |||
{ | { | |||
"cell_type": "markdown", | "cell_type": "markdown", | |||
"metadata": {}, | "metadata": {}, | |||
"source": [ | "source": [ | |||
"This replaces the typical MAP estimation with MCMC sampling, and can take m uch longer depending on how many observations there are - expect several minutes instead of several seconds. If you do full sampling, then you will see the unce rtainty in seasonal components when you plot them:" | "This replaces the typical MAP estimation with MCMC sampling, and can take m uch longer depending on how many observations there are - expect several minutes instead of several seconds. If you do full sampling, then you will see the unce rtainty in seasonal components when you plot them:" | |||
] | ] | |||
}, | }, | |||
{ | { | |||
skipping to change at line 305 | skipping to change at line 309 | |||
"output_type": "display_data" | "output_type": "display_data" | |||
} | } | |||
], | ], | |||
"source": [ | "source": [ | |||
"%%R -w 9 -h 6 -u in\n", | "%%R -w 9 -h 6 -u in\n", | |||
"prophet_plot_components(m, forecast)" | "prophet_plot_components(m, forecast)" | |||
] | ] | |||
}, | }, | |||
{ | { | |||
"cell_type": "code", | "cell_type": "code", | |||
"execution_count": 5, | "execution_count": 8, | |||
"metadata": {}, | "metadata": {}, | |||
"outputs": [ | "outputs": [ | |||
{ | { | |||
"data": { | "data": { | |||
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"text/plain": [ | "text/plain": [ | |||
"<Figure size 648x432 with 2 Axes>" | "<Figure size 648x432 with 2 Axes>" | |||
] | ] | |||
}, | }, | |||
"metadata": {}, | "metadata": {}, | |||
"output_type": "display_data" | "output_type": "display_data" | |||
} | } | |||
], | ], | |||
"source": [ | "source": [ | |||
"fig = m.plot_components(forecast)" | "fig = m.plot_components(forecast)" | |||
skipping to change at line 334 | skipping to change at line 338 | |||
"cell_type": "markdown", | "cell_type": "markdown", | |||
"metadata": {}, | "metadata": {}, | |||
"source": [ | "source": [ | |||
"You can access the raw posterior predictive samples in Python using the met hod `m.predictive_samples(future)`, or in R using the function `predictive_sampl es(m, future)`." | "You can access the raw posterior predictive samples in Python using the met hod `m.predictive_samples(future)`, or in R using the function `predictive_sampl es(m, future)`." | |||
] | ] | |||
} | } | |||
], | ], | |||
"metadata": { | "metadata": { | |||
"celltoolbar": "Edit Metadata", | "celltoolbar": "Edit Metadata", | |||
"kernelspec": { | "kernelspec": { | |||
"display_name": "Python 3", | "display_name": "Python 3 (ipykernel)", | |||
"language": "python", | "language": "python", | |||
"name": "python3" | "name": "python3" | |||
}, | }, | |||
"language_info": { | "language_info": { | |||
"codemirror_mode": { | "codemirror_mode": { | |||
"name": "ipython", | "name": "ipython", | |||
"version": 3 | "version": 3 | |||
}, | }, | |||
"file_extension": ".py", | "file_extension": ".py", | |||
"mimetype": "text/x-python", | "mimetype": "text/x-python", | |||
"name": "python", | "name": "python", | |||
"nbconvert_exporter": "python", | "nbconvert_exporter": "python", | |||
"pygments_lexer": "ipython3", | "pygments_lexer": "ipython3", | |||
"version": "3.8.3" | "version": "3.8.10" | |||
} | } | |||
}, | }, | |||
"nbformat": 4, | "nbformat": 4, | |||
"nbformat_minor": 1 | "nbformat_minor": 4 | |||
} | } | |||
End of changes. 18 change blocks. | ||||
24 lines changed or deleted | 28 lines changed or added |