"Fossies" - the Fresh Open Source Software Archive  

Source code changes of the file "notebooks/uncertainty_intervals.ipynb" between
prophet-1.1.tar.gz and prophet-1.1.1.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.

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": [
{ {
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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

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