"Fossies" - the Fresh Open Source Software Archive  

Source code changes of the file "notebooks/saturating_forecasts.ipynb" between
prophet-0.7.tar.gz and prophet-1.0.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.

saturating_forecasts.ipynb  (prophet-0.7):saturating_forecasts.ipynb  (prophet-1.0)
{ {
"cells": [ "cells": [
{ {
"cell_type": "code", "cell_type": "code",
"execution_count": 1, "execution_count": 1,
"metadata": { "metadata": {
"block_hidden": true "block_hidden": true
}, },
"outputs": [ "outputs": [],
{
"name": "stderr",
"output_type": "stream",
"text": [
"/usr/local/lib/python3.7/site-packages/fbprophet-0.6.1.dev0-py3.7.egg/fbp
rophet/diagnostics.py:10: TqdmExperimentalWarning: Using `tqdm.autonotebook.tqdm
` in notebook mode. Use `tqdm.tqdm` instead to force console mode (e.g. in jupyt
er console)\n",
" from tqdm.autonotebook import tqdm\n"
]
}
],
"source": [ "source": [
"%load_ext rpy2.ipython\n", "%load_ext rpy2.ipython\n",
"%matplotlib inline\n", "%matplotlib inline\n",
"from fbprophet import Prophet\n", "from prophet import Prophet\n",
"import pandas as pd\n", "import pandas as pd\n",
"import logging\n", "import logging\n",
"logging.getLogger('fbprophet').setLevel(logging.ERROR)\n", "logging.getLogger('prophet').setLevel(logging.ERROR)\n",
"import warnings\n", "import warnings\n",
"warnings.filterwarnings(\"ignore\")" "warnings.filterwarnings(\"ignore\")"
] ]
}, },
{ {
"cell_type": "code", "cell_type": "code",
"execution_count": 2, "execution_count": 2,
"metadata": { "metadata": {
"block_hidden": true "block_hidden": true
}, },
skipping to change at line 67 skipping to change at line 58
"### Forecasting Growth\n", "### Forecasting Growth\n",
"\n", "\n",
"By default, Prophet uses a linear model for its forecast. When forecasting growth, there is usually some maximum achievable point: total market size, total population size, etc. This is called the carrying capacity, and the forecast sh ould saturate at this point.\n", "By default, Prophet uses a linear model for its forecast. When forecasting growth, there is usually some maximum achievable point: total market size, total population size, etc. This is called the carrying capacity, and the forecast sh ould saturate at this point.\n",
"\n", "\n",
"Prophet allows you to make forecasts using a [logistic growth](https://en.w ikipedia.org/wiki/Logistic_function) trend model, with a specified carrying capa city. We illustrate this with the log number of page visits to the [R (programmi ng language)](https://en.wikipedia.org/wiki/R_%28programming_language%29) page o n Wikipedia:" "Prophet allows you to make forecasts using a [logistic growth](https://en.w ikipedia.org/wiki/Logistic_function) trend model, with a specified carrying capa city. We illustrate this with the log number of page visits to the [R (programmi ng language)](https://en.wikipedia.org/wiki/R_%28programming_language%29) page o n Wikipedia:"
] ]
}, },
{ {
"cell_type": "code", "cell_type": "code",
"execution_count": 3, "execution_count": 3,
"metadata": { "metadata": {},
"collapsed": true
},
"outputs": [], "outputs": [],
"source": [ "source": [
"%%R\n", "%%R\n",
"df <- read.csv('../examples/example_wp_log_R.csv')" "df <- read.csv('../examples/example_wp_log_R.csv')"
] ]
}, },
{ {
"cell_type": "code", "cell_type": "code",
"execution_count": 4, "execution_count": 2,
"metadata": { "metadata": {},
"collapsed": true
},
"outputs": [], "outputs": [],
"source": [ "source": [
"df = pd.read_csv('../examples/example_wp_log_R.csv')" "df = pd.read_csv('../examples/example_wp_log_R.csv')"
] ]
}, },
{ {
"cell_type": "markdown", "cell_type": "markdown",
"metadata": {}, "metadata": {},
"source": [ "source": [
"We must specify the carrying capacity in a column `cap`. Here we will assum e a particular value, but this would usually be set using data or expertise abou t the market size." "We must specify the carrying capacity in a column `cap`. Here we will assum e a particular value, but this would usually be set using data or expertise abou t the market size."
] ]
}, },
{ {
"cell_type": "code", "cell_type": "code",
"execution_count": 5, "execution_count": 4,
"metadata": { "metadata": {},
"collapsed": true
},
"outputs": [], "outputs": [],
"source": [ "source": [
"%%R\n", "%%R\n",
"df$cap <- 8.5" "df$cap <- 8.5"
] ]
}, },
{ {
"cell_type": "code", "cell_type": "code",
"execution_count": 6, "execution_count": 3,
"metadata": { "metadata": {},
"collapsed": true
},
"outputs": [], "outputs": [],
"source": [ "source": [
"df['cap'] = 8.5" "df['cap'] = 8.5"
] ]
}, },
{ {
"cell_type": "markdown", "cell_type": "markdown",
"metadata": {}, "metadata": {},
"source": [ "source": [
"The important things to note are that `cap` must be specified for every row in the dataframe, and that it does not have to be constant. If the market size is growing, then `cap` can be an increasing sequence.\n", "The important things to note are that `cap` must be specified for every row in the dataframe, and that it does not have to be constant. If the market size is growing, then `cap` can be an increasing sequence.\n",
"\n", "\n",
"We then fit the model as before, except pass in an additional argument to s pecify logistic growth:" "We then fit the model as before, except pass in an additional argument to s pecify logistic growth:"
] ]
}, },
{ {
"cell_type": "code", "cell_type": "code",
"execution_count": 7, "execution_count": 5,
"metadata": { "metadata": {
"output_hidden": true "output_hidden": true
}, },
"outputs": [ "outputs": [
{ {
"data": { "name": "stderr",
"text/plain": [ "output_type": "stream",
"Initial log joint probability = -19.9808\n", "text": [
"Optimization terminated normally: \n", "R[write to console]: Disabling daily seasonality. Run prophet with daily.
" Convergence detected: relative gradient magnitude is below tolerance\n seasonality=TRUE to override this.\n",
" "\n"
] ]
},
"metadata": {},
"output_type": "display_data"
} }
], ],
"source": [ "source": [
"%%R\n", "%%R\n",
"m <- prophet(df, growth = 'logistic')" "m <- prophet(df, growth = 'logistic')"
] ]
}, },
{ {
"cell_type": "code", "cell_type": "code",
"execution_count": 8, "execution_count": 4,
"metadata": { "metadata": {
"output_hidden": true "output_hidden": true
}, },
"outputs": [ "outputs": [
{ {
"name": "stderr",
"output_type": "stream",
"text": [
"INFO:numexpr.utils:NumExpr defaulting to 8 threads.\n"
]
},
{
"data": { "data": {
"text/plain": [ "text/plain": [
"<fbprophet.forecaster.Prophet at 0x7f52e797fb90>" "<prophet.forecaster.Prophet at 0x7f14440461f0>"
] ]
}, },
"execution_count": 8, "execution_count": 4,
"metadata": {}, "metadata": {},
"output_type": "execute_result" "output_type": "execute_result"
} }
], ],
"source": [ "source": [
"m = Prophet(growth='logistic')\n", "m = Prophet(growth='logistic')\n",
"m.fit(df)" "m.fit(df)"
] ]
}, },
{ {
"cell_type": "markdown", "cell_type": "markdown",
"metadata": {}, "metadata": {},
"source": [ "source": [
"We make a dataframe for future predictions as before, except we must also s pecify the capacity in the future. Here we keep capacity constant at the same va lue as in the history, and forecast 5 years into the future:" "We make a dataframe for future predictions as before, except we must also s pecify the capacity in the future. Here we keep capacity constant at the same va lue as in the history, and forecast 5 years into the future:"
] ]
}, },
{ {
"cell_type": "code", "cell_type": "code",
"execution_count": 9, "execution_count": 6,
"metadata": { "metadata": {
"output_hidden": true "output_hidden": true
}, },
"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",
"future <- make_future_dataframe(m, periods = 1826)\n", "future <- make_future_dataframe(m, periods = 1826)\n",
"future$cap <- 8.5\n", "future$cap <- 8.5\n",
"fcst <- predict(m, future)\n", "fcst <- predict(m, future)\n",
"plot(m, fcst)" "plot(m, fcst)"
] ]
}, },
{ {
"cell_type": "code", "cell_type": "code",
"execution_count": 10, "execution_count": 5,
"metadata": {}, "metadata": {},
"outputs": [ "outputs": [
{ {
"data": { "data": {
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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": [
"future = m.make_future_dataframe(periods=1826)\n", "future = m.make_future_dataframe(periods=1826)\n",
skipping to change at line 239 skipping to change at line 226
"source": [ "source": [
"The logistic function has an implicit minimum of 0, and will saturate at 0 the same way that it saturates at the capacity. It is possible to also specify a different saturating minimum.\n", "The logistic function has an implicit minimum of 0, and will saturate at 0 the same way that it saturates at the capacity. It is possible to also specify a different saturating minimum.\n",
"\n", "\n",
"### Saturating Minimum\n", "### Saturating Minimum\n",
"\n", "\n",
"The logistic growth model can also handle a saturating minimum, which is sp ecified with a column `floor` in the same way as the `cap` column specifies the maximum:" "The logistic growth model can also handle a saturating minimum, which is sp ecified with a column `floor` in the same way as the `cap` column specifies the maximum:"
] ]
}, },
{ {
"cell_type": "code", "cell_type": "code",
"execution_count": 11, "execution_count": 7,
"metadata": { "metadata": {
"output_hidden": true "output_hidden": true
}, },
"outputs": [ "outputs": [
{ {
"data": { "name": "stderr",
"text/plain": [ "output_type": "stream",
"Initial log joint probability = -109.241\n", "text": [
"Optimization terminated normally: \n", "R[write to console]: Disabling daily seasonality. Run prophet with daily.
" Convergence detected: relative gradient magnitude is below tolerance\n seasonality=TRUE to override this.\n",
" "\n"
] ]
},
"metadata": {},
"output_type": "display_data"
}, },
{ {
"data": { "data": {
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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",
"df$y <- 10 - df$y\n", "df$y <- 10 - df$y\n",
"df$cap <- 6\n", "df$cap <- 6\n",
"df$floor <- 1.5\n", "df$floor <- 1.5\n",
"future$cap <- 6\n", "future$cap <- 6\n",
"future$floor <- 1.5\n", "future$floor <- 1.5\n",
"m <- prophet(df, growth = 'logistic')\n", "m <- prophet(df, growth = 'logistic')\n",
"fcst <- predict(m, future)\n", "fcst <- predict(m, future)\n",
"plot(m, fcst)" "plot(m, fcst)"
] ]
}, },
{ {
"cell_type": "code", "cell_type": "code",
"execution_count": 12, "execution_count": 6,
"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": [
"df['y'] = 10 - df['y']\n", "df['y'] = 10 - df['y']\n",
skipping to change at line 327 skipping to change at line 311
"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": "3.7.8" "version": "3.8.3"
} }
}, },
"nbformat": 4, "nbformat": 4,
"nbformat_minor": 1 "nbformat_minor": 1
} }
 End of changes. 23 change blocks. 
63 lines changed or deleted 44 lines changed or added

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