2017-02-22 23:59:43 +00:00
{
"cells": [
{
"cell_type": "code",
2017-07-05 05:39:57 +00:00
"execution_count": 1,
2017-02-22 23:59:43 +00:00
"metadata": {
2018-05-28 19:37:23 +00:00
"block_hidden": true
2017-02-22 23:59:43 +00:00
},
"outputs": [],
"source": [
"%load_ext rpy2.ipython\n",
"%matplotlib inline\n",
"from fbprophet import Prophet\n",
2017-09-02 05:46:11 +00:00
"import pandas as pd\n",
"import logging\n",
"logging.getLogger('fbprophet').setLevel(logging.ERROR)\n",
"import warnings\n",
"warnings.filterwarnings(\"ignore\")"
2017-02-22 23:59:43 +00:00
]
},
{
"cell_type": "code",
"execution_count": 2,
"metadata": {
2018-05-28 19:37:23 +00:00
"block_hidden": true
2017-02-22 23:59:43 +00:00
},
2017-09-02 05:46:11 +00:00
"outputs": [],
2017-02-22 23:59:43 +00:00
"source": [
"%%R\n",
"library(prophet)"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
2017-07-05 03:03:21 +00:00
"## Sub-daily data\n",
"\n",
2017-09-02 05:46:11 +00:00
"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):"
2017-07-05 03:03:21 +00:00
]
},
{
"cell_type": "code",
2017-07-11 05:57:13 +00:00
"execution_count": 3,
2017-09-02 05:46:11 +00:00
"metadata": {
2017-09-02 20:07:49 +00:00
"output_hidden": true
2017-09-02 05:46:11 +00:00
},
2017-07-11 05:57:13 +00:00
"outputs": [
{
"data": {
"text/plain": [
2018-05-28 19:37:23 +00:00
"Initial log joint probability = -1492.46\n",
2017-07-11 05:57:13 +00:00
"Optimization terminated normally: \n",
2018-05-28 19:37:23 +00:00
" Convergence detected: relative gradient magnitude is below tolerance\n"
2017-07-11 05:57:13 +00:00
]
},
"metadata": {},
"output_type": "display_data"
},
{
"data": {
2018-05-28 19:37:23 +00:00
"image/png": "iVBORw0KGgoAAAANSUhEUgAAAtAAAAGwCAIAAAAPKcUMAAAACXBIWXMAAAsSAAALEgHS3X78AAAg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\nRAXgfYS0tDQ260F9NTj
2017-07-11 05:57:13 +00:00
},
"metadata": {},
"output_type": "display_data"
}
],
"source": [
"%%R -w 10 -h 6 -u in\n",
"df <- read.csv('../examples/example_yosemite_temps.csv')\n",
"m <- prophet(df, changepoint.prior.scale=0.01)\n",
"future <- make_future_dataframe(m, periods = 300, freq = 60 * 60)\n",
"fcst <- predict(m, future)\n",
"plot(m, fcst);"
]
},
{
"cell_type": "code",
"execution_count": 4,
2018-05-28 19:37:23 +00:00
"metadata": {},
2017-07-05 03:03:21 +00:00
"outputs": [
{
"data": {
2018-05-28 19:37:23 +00:00
"image/png": "iVBORw0KGgoAAAANSUhEUgAAAscAAAGoCAYAAACqvEg8AAAABHNCSVQICAgIfAhkiAAAAAlwSFlz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\nrJhevkAIUHQICpoQvuO
2017-07-05 03:03:21 +00:00
"text/plain": [
2018-05-28 19:37:23 +00:00
"<Figure size 720x432 with 1 Axes>"
2017-07-05 03:03:21 +00:00
]
},
"metadata": {},
"output_type": "display_data"
}
],
"source": [
"df = pd.read_csv('../examples/example_yosemite_temps.csv')\n",
"m = Prophet(changepoint_prior_scale=0.01).fit(df)\n",
"future = m.make_future_dataframe(periods=300, freq='H')\n",
"fcst = m.predict(future)\n",
"m.plot(fcst);"
]
},
2017-09-02 05:46:11 +00:00
{
"cell_type": "markdown",
"metadata": {},
"source": [
"The daily seasonality will show up in the components plot:"
]
},
2017-07-05 05:39:57 +00:00
{
"cell_type": "code",
2017-07-11 05:57:13 +00:00
"execution_count": 5,
2017-09-02 05:46:11 +00:00
"metadata": {
2017-09-02 20:07:49 +00:00
"output_hidden": true
2017-09-02 05:46:11 +00:00
},
2017-07-11 05:57:13 +00:00
"outputs": [
{
"data": {
2018-05-28 19:37:23 +00:00
"image/png": "iVBORw0KGgoAAAANSUhEUgAAAogAAAKICAIAAAB8K5ztAAAACXBIWXMAAAsSAAALEgHS3X78AAAg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\nENJdiDOxdgadEueJEKp
2017-07-11 05:57:13 +00:00
},
"metadata": {},
"output_type": "display_data"
}
],
"source": [
"%%R -w 9 -h 9 -u in\n",
"prophet_plot_components(m, fcst)"
]
},
{
"cell_type": "code",
"execution_count": 6,
2018-05-28 19:37:23 +00:00
"metadata": {},
2017-07-05 05:39:57 +00:00
"outputs": [
{
"data": {
2018-05-28 19:37:23 +00:00
"image/png": "iVBORw0KGgoAAAANSUhEUgAAAn8AAAKACAYAAADtih43AAAABHNCSVQICAgIfAhkiAAAAAlwSFlz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\nRLrVrEy9Vo2x0UEYGx3
2017-07-05 05:39:57 +00:00
"text/plain": [
2018-05-28 19:37:23 +00:00
"<Figure size 648x648 with 3 Axes>"
2017-07-05 05:39:57 +00:00
]
},
"metadata": {},
"output_type": "display_data"
}
],
"source": [
"m.plot_components(fcst);"
]
},
2017-07-05 03:03:21 +00:00
{
"cell_type": "markdown",
"metadata": {},
"source": [
"## Monthly data\n",
"\n",
"You can use Prophet to fit monthly data. However, the underlying model is continuous-time, which means that you can get strange results if you fit the model to monthly data and then ask for daily forecasts. Here we forecast US retail sales volume for the next 10 years:"
2017-02-22 23:59:43 +00:00
]
},
{
"cell_type": "code",
2017-07-11 05:57:13 +00:00
"execution_count": 7,
2017-02-22 23:59:43 +00:00
"metadata": {
"output_hidden": true
},
"outputs": [
{
"data": {
"text/plain": [
2018-05-28 19:37:23 +00:00
"Initial log joint probability = -50.4117\n",
2017-02-22 23:59:43 +00:00
"Optimization terminated normally: \n",
2018-05-28 19:37:23 +00:00
" Convergence detected: relative gradient magnitude is below tolerance\n"
2017-02-22 23:59:43 +00:00
]
},
"metadata": {},
"output_type": "display_data"
},
{
"data": {
2018-05-28 19:37:23 +00:00
"image/png": "iVBORw0KGgoAAAANSUhEUgAAAtAAAAGwCAIAAAAPKcUMAAAACXBIWXMAAAsSAAALEgHS3X78AAAg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\n+3cDFTZisnymGhxRxRZ
2017-02-22 23:59:43 +00:00
},
"metadata": {},
"output_type": "display_data"
}
],
"source": [
"%%R -w 10 -h 6 -u in\n",
"df <- read.csv('../examples/example_retail_sales.csv')\n",
2018-05-28 19:37:23 +00:00
"m <- prophet(df, seasonality.mode = 'multiplicative')\n",
2017-02-22 23:59:43 +00:00
"future <- make_future_dataframe(m, periods = 3652)\n",
"fcst <- predict(m, future)\n",
"plot(m, fcst);"
]
},
{
"cell_type": "code",
2017-07-11 05:57:13 +00:00
"execution_count": 8,
2018-05-28 19:37:23 +00:00
"metadata": {},
2017-02-22 23:59:43 +00:00
"outputs": [
{
"data": {
2018-05-28 19:37:23 +00:00
"image/png": "iVBORw0KGgoAAAANSUhEUgAAAsgAAAGoCAYAAABbtxOxAAAABHNCSVQICAgIfAhkiAAAAAlwSFlz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\nK8BKwQ8lQqkwMFnGS8e
2017-02-22 23:59:43 +00:00
"text/plain": [
2018-05-28 19:37:23 +00:00
"<Figure size 720x432 with 1 Axes>"
2017-02-22 23:59:43 +00:00
]
},
"metadata": {},
"output_type": "display_data"
}
],
"source": [
"df = pd.read_csv('../examples/example_retail_sales.csv')\n",
2018-05-28 19:37:23 +00:00
"m = Prophet(seasonality_mode='multiplicative').fit(df)\n",
2017-02-22 23:59:43 +00:00
"future = m.make_future_dataframe(periods=3652)\n",
"fcst = m.predict(future)\n",
"m.plot(fcst);"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"The forecast here seems very noisy. What's happening is that this particular data set only provides monthly data. When we fit the yearly seasonality, it only has data for the first of each month and the seasonality components for the remaining days are unidentifiable and overfit. When you are fitting Prophet to monthly data, only make monthly forecasts, which can be done by passing the frequency into make_future_dataframe:"
]
},
{
"cell_type": "code",
2017-07-11 05:57:13 +00:00
"execution_count": 9,
2017-02-22 23:59:43 +00:00
"metadata": {
"output_hidden": true
},
"outputs": [
{
"data": {
2018-05-28 19:37:23 +00:00
"image/png": "iVBORw0KGgoAAAANSUhEUgAAAtAAAAGwCAIAAAAPKcUMAAAACXBIWXMAAAsSAAALEgHS3X78AAAg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\nQIYDAADgkH/YsPf7m7t
2017-02-22 23:59:43 +00:00
},
"metadata": {},
"output_type": "display_data"
}
],
"source": [
"%%R -w 10 -h 6 -u in\n",
2017-07-11 05:57:13 +00:00
"future <- make_future_dataframe(m, periods = 120, freq = 'month')\n",
2017-02-22 23:59:43 +00:00
"fcst <- predict(m, future)\n",
"plot(m, fcst)"
]
},
{
"cell_type": "code",
2017-07-11 05:57:13 +00:00
"execution_count": 10,
2018-05-28 19:37:23 +00:00
"metadata": {},
2017-02-22 23:59:43 +00:00
"outputs": [
{
"data": {
2018-05-28 19:37:23 +00:00
"image/png": "iVBORw0KGgoAAAANSUhEUgAAAsgAAAGoCAYAAABbtxOxAAAABHNCSVQICAgIfAhkiAAAAAlwSFlz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\nHSK+WXQPX2q8fHIF6/h
2017-02-22 23:59:43 +00:00
"text/plain": [
2018-05-28 19:37:23 +00:00
"<Figure size 720x432 with 1 Axes>"
2017-02-22 23:59:43 +00:00
]
},
"metadata": {},
"output_type": "display_data"
}
],
"source": [
"future = m.make_future_dataframe(periods=120, freq='M')\n",
"fcst = m.predict(future)\n",
"m.plot(fcst);"
]
2018-05-28 19:37:23 +00:00
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"The same principle applies to other datasets with irregular spacing of the data. For example, if the history contains only weekdays, then predictions should only be made for weekdays since the weekly seasonality will not be estimated for the weekends."
]
2017-02-22 23:59:43 +00:00
}
],
"metadata": {
"kernelspec": {
"display_name": "Python 2",
"language": "python",
"name": "python2"
},
"language_info": {
"codemirror_mode": {
"name": "ipython",
"version": 2
},
"file_extension": ".py",
"mimetype": "text/x-python",
"name": "python",
"nbconvert_exporter": "python",
"pygments_lexer": "ipython2",
"version": "2.7.13"
}
},
"nbformat": 4,
2017-07-05 03:03:21 +00:00
"nbformat_minor": 1
2017-02-22 23:59:43 +00:00
}
