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{
"cells": [
{
"cell_type": "code",
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"execution_count": 1,
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"metadata": {
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"block_hidden": true,
"collapsed": true
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},
"outputs": [],
"source": [
"%load_ext rpy2.ipython\n",
"%matplotlib inline\n",
"from fbprophet import Prophet\n",
"import pandas as pd"
]
},
{
"cell_type": "code",
"execution_count": 2,
"metadata": {
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"block_hidden": true
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},
"outputs": [
{
"name": "stderr",
"output_type": "stream",
"text": [
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"/usr/local/lib/python2.7/dist-packages/rpy2/rinterface/__init__.py:186: RRuntimeWarning: Loading required package: Rcpp\n",
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"\n",
" warnings.warn(x, RRuntimeWarning)\n"
]
}
],
"source": [
"%%R\n",
"library(prophet)"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
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"## Sub-daily data\n",
"\n",
"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:"
]
},
{
"cell_type": "code",
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"execution_count": 3,
"metadata": {},
"outputs": [
{
"data": {
"text/plain": [
"Initial log joint probability = -1444.46\n",
"Optimization terminated normally: \n",
" Convergence detected: relative gradient magnitude is below tolerance\n",
"\r",
"|=========================== | 50% ~2 s remaining \r",
"|======================================================|100% ~0 s remaining "
]
},
"metadata": {},
"output_type": "display_data"
},
{
"data": {
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},
"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,
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"metadata": {},
"outputs": [
{
"name": "stderr",
"output_type": "stream",
"text": [
"INFO:fbprophet.forecaster:Disabling yearly seasonality. Run prophet with yearly_seasonality=True to override this.\n"
]
},
{
"data": {
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"image/png": "iVBORw0KGgoAAAANSUhEUgAAAscAAAGoCAYAAACqvEg8AAAABHNCSVQICAgIfAhkiAAAAAlwSFlz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\nNElPZb02evRo9veYMWM
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"text/plain": [
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"<matplotlib.figure.Figure at 0x7fa933a56e90>"
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]
},
"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);"
]
},
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{
"cell_type": "code",
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"execution_count": 5,
"metadata": {},
"outputs": [
{
"data": {
"image/png": "iVBORw0KGgoAAAANSUhEUgAAAogAAAKICAIAAAB8K5ztAAAACXBIWXMAAAsSAAALEgHS3X78AAAg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\nVmmz4XKVJqtas+qc6lq
},
"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,
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"metadata": {},
"outputs": [
{
"data": {
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"image/png": "iVBORw0KGgoAAAANSUhEUgAAAn8AAAKACAYAAADtih43AAAABHNCSVQICAgIfAhkiAAAAAlwSFlz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\ngn1OKggDSI8JxM9L3hb
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"text/plain": [
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"<matplotlib.figure.Figure at 0x7fa933a56710>"
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]
},
"metadata": {},
"output_type": "display_data"
}
],
"source": [
"m.plot_components(fcst);"
]
},
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{
"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:"
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]
},
{
"cell_type": "code",
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"execution_count": 7,
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"metadata": {
"output_hidden": true
},
"outputs": [
{
"data": {
"text/plain": [
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"Initial log joint probability = -2.41173\n",
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"Optimization terminated normally: \n",
" Convergence detected: relative gradient magnitude is below tolerance\n"
]
},
"metadata": {},
"output_type": "display_data"
},
{
"data": {
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"image/png": "iVBORw0KGgoAAAANSUhEUgAAAtAAAAGwCAIAAAAPKcUMAAAACXBIWXMAAAsSAAALEgHS3X78AAAg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\neIScI9USO418bbGYRCw
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},
"metadata": {},
"output_type": "display_data"
}
],
"source": [
"%%R -w 10 -h 6 -u in\n",
"df <- read.csv('../examples/example_retail_sales.csv')\n",
"m <- prophet(df)\n",
"future <- make_future_dataframe(m, periods = 3652)\n",
"fcst <- predict(m, future)\n",
"plot(m, fcst);"
]
},
{
"cell_type": "code",
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"execution_count": 8,
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"metadata": {},
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"outputs": [
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{
"name": "stderr",
"output_type": "stream",
"text": [
"INFO:fbprophet.forecaster:Disabling weekly seasonality. Run prophet with weekly_seasonality=True to override this.\n",
"INFO:fbprophet.forecaster:Disabling daily seasonality. Run prophet with daily_seasonality=True to override this.\n"
]
},
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{
"data": {
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"text/plain": [
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"<matplotlib.figure.Figure at 0x7fa945045890>"
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]
},
"metadata": {},
"output_type": "display_data"
}
],
"source": [
"df = pd.read_csv('../examples/example_retail_sales.csv')\n",
"m = Prophet().fit(df)\n",
"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",
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"execution_count": 9,
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"metadata": {
"output_hidden": true
},
"outputs": [
{
"data": {
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"image/png": "iVBORw0KGgoAAAANSUhEUgAAAtAAAAGwCAIAAAAPKcUMAAAACXBIWXMAAAsSAAALEgHS3X78AAAg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\nmSW+3U2a/UARg3rFVNM
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},
"metadata": {},
"output_type": "display_data"
}
],
"source": [
"%%R -w 10 -h 6 -u in\n",
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"future <- make_future_dataframe(m, periods = 120, freq = 'month')\n",
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"fcst <- predict(m, future)\n",
"plot(m, fcst)"
]
},
{
"cell_type": "code",
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"execution_count": 10,
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"metadata": {},
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"outputs": [
{
"data": {
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"image/png": "iVBORw0KGgoAAAANSUhEUgAAAsgAAAGoCAYAAABbtxOxAAAABHNCSVQICAgIfAhkiAAAAAlwSFlz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\nWZqAHb8fALywvR7ialR
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"text/plain": [
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"<matplotlib.figure.Figure at 0x7fa964c17bd0>"
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]
},
"metadata": {},
"output_type": "display_data"
}
],
"source": [
"future = m.make_future_dataframe(periods=120, freq='M')\n",
"fcst = m.predict(future)\n",
"m.plot(fcst);"
]
}
],
"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,
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"nbformat_minor": 1
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}
