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": {
2017-07-05 05:39:57 +00:00
"block_hidden": true,
2017-09-02 05:46:11 +00:00
"collapsed": false
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": {
2017-09-02 05:46:11 +00:00
"block_hidden": true,
"collapsed": false
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
"collapsed": false,
"output_hidden": true
2017-09-02 05:46:11 +00:00
},
2017-07-11 05:57:13 +00:00
"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",
2017-09-02 05:46:11 +00:00
"|============= | 25% ~9 s remaining \r",
"|=========================== | 50% ~6 s remaining \r",
"|======================================== | 75% ~3 s remaining \r",
2017-07-11 05:57:13 +00:00
"|======================================================|100% ~0 s remaining "
]
},
"metadata": {},
"output_type": "display_data"
},
{
"data": {
2017-09-02 05:46:11 +00:00
"image/png": "iVBORw0KGgoAAAANSUhEUgAAAtAAAAGwCAIAAAAPKcUMAAAACXBIWXMAAAsSAAALEgHS3X78AAAg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\n2bOHLlNag6CEgzsDlLh
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,
2017-09-02 05:46:11 +00:00
"metadata": {
"collapsed": false
},
2017-07-05 03:03:21 +00:00
"outputs": [
{
"data": {
2017-09-02 05:46:11 +00:00
"image/png": "iVBORw0KGgoAAAANSUhEUgAAAscAAAGpCAYAAABh4JuZAAAABHNCSVQICAgIfAhkiAAAAAlwSFlz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\n4WuOTfnorwCWZeG0007
2017-07-05 03:03:21 +00:00
"text/plain": [
2017-09-02 05:46:11 +00:00
"<matplotlib.figure.Figure at 0x7f35bcb6b690>"
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
"collapsed": false,
"output_hidden": true
2017-09-02 05:46:11 +00:00
},
2017-07-11 05:57:13 +00:00
"outputs": [
{
"data": {
2017-09-02 05:46:11 +00:00
"image/png": "iVBORw0KGgoAAAANSUhEUgAAAogAAAKICAIAAAB8K5ztAAAACXBIWXMAAAsSAAALEgHS3X78AAAg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\nXeCNDgC8dqlGd6CgYfH
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,
2017-09-02 05:46:11 +00:00
"metadata": {
"collapsed": false
},
2017-07-05 05:39:57 +00:00
"outputs": [
{
"data": {
2017-09-02 05:46:11 +00:00
"image/png": "iVBORw0KGgoAAAANSUhEUgAAAoAAAAKBCAYAAADHkRqGAAAABHNCSVQICAgIfAhkiAAAAAlwSFlz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\nZuHqQ1ha5peQEOjFwIR
2017-07-05 05:39:57 +00:00
"text/plain": [
2017-09-02 05:46:11 +00:00
"<matplotlib.figure.Figure at 0x7f3584c64050>"
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": {
2017-09-02 05:46:11 +00:00
"collapsed": false,
2017-02-22 23:59:43 +00:00
"output_hidden": true
},
"outputs": [
{
"data": {
"text/plain": [
2017-07-11 05:57:13 +00:00
"Initial log joint probability = -2.41173\n",
2017-02-22 23:59:43 +00:00
"Optimization terminated normally: \n",
2017-09-02 05:46:11 +00:00
" Convergence detected: relative gradient magnitude is below tolerance\n",
"\r",
"|======================================================|100% ~0 s remaining "
2017-02-22 23:59:43 +00:00
]
},
"metadata": {},
"output_type": "display_data"
},
{
"data": {
2017-09-02 05:46:11 +00:00
"image/png": "iVBORw0KGgoAAAANSUhEUgAAAtAAAAGwCAIAAAAPKcUMAAAACXBIWXMAAAsSAAALEgHS3X78AAAg\nAElEQVR4nOy9eZwd1XXvu9beVXWmniS1WmqJQQIhJhkxGQO+NoZgG+MPzy+eMM/2DX4hISE4uTj3\nxUmu/Z59Yz8I9rX5QOzrG8eJb3Ltx+QhsZ04CWBAgWAmI8xkiUEDUmvq+YxVe6/1/thV1Ufdp6VT\nTQ9Sa30/fPQ5XV371D4l1PXrNfwWMjMIgiAIgiDMJWqhNyAIgiAIwuJHBIcgCIIgCHOOCA5BEARB\nEOYcERyCIAiCIMw53kJv4PBUKpWF3sJ8o5Ri5mO8nlcppZQyxiz0RhYYrbW1dqF3scB4nkdERLTQ\nG1lIEBER5SZ4nhdF0UJvZIE58n8slEqlqQePAsFRq9UWegvzTT6fD8PwGP/JksvlcrncMfi3P4lS\nqSQ3obu7OwzDRqOx0BtZSLTWnufJTSgUCmNjYwu9kQXmyP+x0FJwSEpFEARBEIQ5RwSHIAiCIAhz\njggOQRAEQRDmnFmr4ahUKrfccou1tq+v7w/+4A8Q8RAnR1F0++23l8vlE0888dprrz1w4MCnPvWp\nvr4+ALjppptWr149W7sSBEEQBOFIYNYEx6ZNm84555z3v//9X/nKV7Zu3bp+/fpDnPzYY4+tWrXq\nmmuuufnmm3fu3Dk+Pv7e97736quvnq3NCIIgCIJwRDFrgmP58uWbNm0aGhoaHBzs6ekpl8u33XZb\nGIbLli278cYbtdbNJ2/dunXDhg0AsHbt2q1btyqldu3adccdd2zYsOHSSy+drS0JgiAIgnCEMGuC\nY926dd/+9re/9KUv+b7f09Pzgx/84JJLLnnb29527733btq06R3veAcAfP3rX7/hhhsAoFqtLlu2\nDAB6e3srlUpfX9+GDRvOPffc2267benSpRs3bnz11Ve//OUvA8B73vOeK664YrY2ebSglMrlcgu9\niwXG+XB0d3cv9EYWGNcMudC7WGA8zysWi/l8fqE3ssAopY7xm+DMSOTHwhH+Y2E6TwecLXepb37z\nm+ecc87555//ve99r7Oz84UXXqjVah0dHQBw0UUXFQqFhx566Pnnnz/zzDPPOeecF198ccOGDRdc\ncMFdd921fPnyyy67zL3JAw88MDg4+KEPfWh0dPSJJ54AgLVr17rajmMK3/eNMce48Zfv+57nHeG9\n5vNALpc7xq0XAKBQKERRdIy7wIkVHgAopYrFYrlcXuiNLDBH+I8FZu7q6pp6fNYkkjHGiRoiMsas\nWrWqp6fnXe961yOPPNLf37969eozzzwzjXAYY7Zt23bBBRds37794osv/u53v3vGGWecffbZO3bs\nWLduHQB0d3dffvnl7p0PHDgwW5s8WkBEMf4CAKXUkfyPan4QrycAyOfzxphj/D6I8RcAaK2LxeIx\nfhPgqP2xMGttsR/4wAd++MMffvazn92yZctll1125ZVXPvroo1/4wheee+65VatWTTr5wgsv3LVr\n16233trX13f88cdffvnld95552c+85mRkZGLLrpotrYkCIIgCMIRwqylVOaOYzDCIdbmkFibi4dx\nqVQ6BscJTaK7u7terx+Nv9LNIhLhAACtdU9Pz+Dg4EJvZIE58n8s9Pb2Tj0oxl+CIAiCIMw5IjgE\nQRAEQZhzRHAIgiAIgjDniOAQBEEQBGHOEcEhCIIgCMKcI4JDEARBEIQ5RwSHIAiCIAhzjggOQRAE\nQRDmHBEcgiAIgiDMOSI4BEEQBEGYc0RwCIIgCIIw54jgEARBEARhzhHBIQiCIAjCnCOCQxAEQRAW\nM5sHygu9BQARHIIgCIIgzAMiOARBEARBmHNEcAiCIAiCMOeI4BAEQRAEYc4RwSEIgiAIwpwjgkMQ\nBEEQhDlHBIcgCIIgCHOOCA5BEARBEOYcERyCIAiCIMw5IjgEQRAEQZhzRHAIgiAIgjDniOAQBEEQ\nBGHOEcEhCIIgCMKcI4JDEARBEIQ5RwSHIAiCIBwdHCGD5meGCA5BEARBEOYcERyCIAiCIMw5IjgE\nQRAEQZhzRHAIgiAIgjDniOAQBEEQhGOIhao8FcEhCIIgCMKcI4JDEARBEI4tFiTIIYJDEARBEIQ5\nRwSHIAiCIAhzjggOQRAEQTjmmP+siggOQRAEQVgA5uGRv3mgfOS4oYvgEARBEIRFy8tDta89vnuh\ndwEggkMQBEEQFjGvDTce3zm+0LsAEMEhCIIgCIuYurF1wwu9CwARHIIgCIKwiAkth0QLvQsAERyC\nIAiCsIiJLBuSCIcgCIIgCHOJIY6sCA5BEARBEOYSQ2xZBIcgCIIgCNlp313DEDODPQKyKiI4BEEQ\nBGHR4tIpR0KfirfQGzg8vu8v9BbmG6217/t0ZNQVLxRaa6XUMfi3Pwm5CQCAiO4fxUJvZCFRSnme\nd4z/WFBKwSJ6KHiel/Wz/OCloa5i7qxO1eZaz/NcaOOXe6sFX5+9qtMddN+d5zspEQ5BEARBODr4\n6Zahx18fm3r8md3jz+xu7e7lCjiOhLLRoyDCEUXRQm9hvtFaR1Ekv8oQ0TH4tz+JIAjkJjCztfYY\nvw9aazgmfx42s8hugjEm62epRTRej4ho0lpjDLS6M8aYyBAANCKTV+xOcCe3PH9OkQiHIAiCIBwF\nbB4oh5Zqkc20ysU2SIpGBUEQBEFok5B4kt5ongfbsnXFEgEAgQgOQRAEQRDawxCFNlu23YU2bLaw\nyJwggkMQBEEQjg4O61PeHPBwuKLRIyHCcRQUjQqCIAiCAACWIHOEg9gthCx2YXOBRDgEQRAE4SiA\nmYkzT2KLi0aPAHdzERyCIAiCMN+UQ3vfKyOZlhgCyO6o4aSGCA5BEARBOBZ5atf4N54YyLTEVWOY\njIrDzkimzAUiOARBEARhvimHVIuyVWPEgiNzSkUiHIIgCIJwrFI3FNJBMuCwFZ3Wun6TbDjP6iPB\nuVoEhyAIgiBMMINWjhksCS0zQ5RFCLi0SNZB8xZmIlPmAhEcgiAIgjDfuO7WKEtthfPSaD+l4mSQ\nWJsLgiAIwrFLRJz+OYnp4iXOLbSdCEez/ZeTGkdChEOMvwRBEARhvnGxjUnhikOkZjYPlF2EI2uo\nQtpiBUEQBOHYJcqeUolrODJKBzpiUioS4RAEQRCEN8Tu8caJS/I9+XYfqZsHyq+PNWD6gow01LGx\nvyM9mJiUZ/XhiOMizAyAiJlWzyYS4RAEQRCEN8TXfj5w1y/3Z1ridECmcMWkLpWpc9paEkc4GP7H\nk3u+8+y+TJucXURwCIIgCMIbohzSeCPbAPjYADRLMefMfDjSabH7K9HeSgQA20fqGd9jdhDBIQiC\nIAhviIahupmZbehhVjXHMJyjxkjN/GLXWPsXImKFyMy1iBoRDYyHv/33W7Palc4KIjgEQRAE4Q0R\nETcyCo6keSTTkomFbVKLyDJ4CixD3VLD0ljDWoZymC0eMyuI4BAEQRCEN0RkuaWjxiFwyZRMkQZL\nBFkEx7aRxrXf30LAnkZmiCxHNhZGWeMxs4IIDkEQBEF4Q0REmRpcIUmpZGo5Icb0z3Y4UI0Ga1Fo\n2EdkZic4QmIA2Dbc+Metw5k2/MYRwSEIgiAIbwhLnGkqCkxMjc8kOLJZeNUiCwDl0HpKWQZDHBE5\nS/WHt4/+r817M234jSOCQxAEQRDeEIYha47CssuPZFiSVXA0TGxm6mskZkNkKd7n/ko0XDPZdvyG\nEcEhCIIgCBNkbXAFAGvZOYdONcaYziqDYlONLFdJHDXaJExO9TUygyEwzMYSAIzWTWR5nktHRXAI\ngiAIQswrQ/WP3furrKss88wMQNtJqaQGX7FnKHBk+YYfvXzYvpgokTOeQpdSscSuTHW8YQBgZH6D\nHCI4BEEQBCFmqBZVIhtmrwDNuGKybWg7JFNRYKgabRmsHagay+wyLKN1u2n76KTz0xYYXyEzR0Qm\nEUbliABgSASHIAiCICw
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",
"m <- prophet(df)\n",
"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,
2017-09-02 05:46:11 +00:00
"metadata": {
"collapsed": false
},
2017-02-22 23:59:43 +00:00
"outputs": [
{
"data": {
2017-09-02 05:46:11 +00:00
"image/png": "iVBORw0KGgoAAAANSUhEUgAAAsgAAAGpCAYAAACQ68AUAAAABHNCSVQICAgIfAhkiAAAAAlwSFlz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\nOjs70dPTM+3rnZ2dFa9
2017-02-22 23:59:43 +00:00
"text/plain": [
2017-09-02 05:46:11 +00:00
"<matplotlib.figure.Figure at 0x7f35c0c47f90>"
2017-02-22 23:59:43 +00:00
]
},
"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",
2017-07-11 05:57:13 +00:00
"execution_count": 9,
2017-02-22 23:59:43 +00:00
"metadata": {
2017-09-02 05:46:11 +00:00
"collapsed": false,
2017-02-22 23:59:43 +00:00
"output_hidden": true
},
"outputs": [
{
"data": {
2017-09-02 05:46:11 +00:00
"image/png": "iVBORw0KGgoAAAANSUhEUgAAAtAAAAGwCAIAAAAPKcUMAAAACXBIWXMAAAsSAAALEgHS3X78AAAg\nAElEQVR4nOzdaZwU1dUw8FO3tt5n39kZQJBdEDdkUZGocVc0SozGaDQSozGaPDEmJhrzGvVRE42J\n+rgrRE3co1FAFMUNcURFGZZhmelhgFm6q7rWe+v9UD1NT89CF87KnP8Hfz3Vfbura8buw7nnnss5\njgMIIYQQQj2J9PUJIIQQQujghwEHQgghhHocBhwIIYQQ6nEYcCCEEEKoxwl9fQL7p6pqX59CbyOE\nOI4zyOt5CSGEENu2+/pE+hjP85TSvj6LPiYIAmOMMdbXJ9KXOI7jOA4vgiAIlmX19Yn0sf7/sRAM\nBtsfHAABh6ZpfX0Kvc3n85mmOcg/WWRZlmV5EP72MwSDQbwIOTk5pmkahtHXJ9KXeJ4XBAEvgt/v\nj8VifX0ifaz/fyx0GHDglApCCCGEehwGHAghhBDqcRhwIIQQQqjHYcCBEEIIoR6HAQdCCCGEehwG\nHAghhBDqcRhwIIQQQqjHYcCBEEIIoR6HAQdCCCGEehwGHAghhBDqcRhwIIQQQqjHYcCBEEIIoR6H\nAQdCCCGEehwGHAghhBDqcRhwIIQQQqjHYcCBEEIIoR6HAQdCCCGEehwGHAghhBDqcRhwIIQQQqjH\nYcCBEEIIoR6HAQdCCCF0MKuKKn19CgAYcCCEEEKoF2DAgRBCCKEehwEHQgghhHocBhwIIYQQ6nEY\ncCCEEEKox2HAgRBCCKEehwEHQgghhHocBhwIIYQQ6nEYcCCEEEKox2HAgRBCCKEehwEHQgghhHoc\nBhwIIYTQoNP7G6xgwIEQQgihHocBB0IIIYR6HAYcCCGEEOpxGHAghBBCqMdhwIEQQgihHocBB0II\nIYR6HAYcCCGE0IDx7ZezVkWV3l8TCwBC77+kVzzP9/Up9DZCCM/zHMf19Yn0JUIIx3GD8LefAS+C\ny/2foq/Poi/xPI8XgRACg/JLIZ3Xz8bP6uIZfznuZYQeu5KO43R4fAAEHKIo9vUp9Dae5x3H6ex3\nNki4H6+D8LefAS8CtEYbg/w6EELwj8H9phzkF0EQBE9/CYIgAMAXDdq0ikj6EeixKzmAAw5d1/v6\nFPqAaZqMsb4+i74kyzIhZHD+9tPxPI8XQZZly7IMw+jrE+lLPM8LgoAXwe/3D/L/I0zTpJRmfxFM\n03RvpIa0P9LtwuFw+4NYw4EQQgihHocBB0IIIXTw6JOC0GxgwIEQQggNbJ0FGf0q+MCAAyGEEBos\n+jAEGQBFowghhBCCLsOFfpXM6BAGHAghhNBBpX8GHzilghBCCKEehwEHQggh1Af6Zx6i52DAgRBC\nCKEehzUcCCGE0EA1gNIkmOFACCGEBqQBFG0ABhwIIYQQ6gUYcCCEEEKox2HAgRBCCB3MWnTayY7x\nvQoDDoQQQuhgdv1/t3y1W+3rs8CAAyGEEDqoxQyqWX2f4sCAAyGEEOqnumUdimYxk7Jv/zzfEvbh\nQAghhAaAAw4+NIua1OnzNbSY4UAIIYT6o24JEUzqUAeMfpDhwIADIYQQ6kc6jDO+RXqDAQBOqSCE\nEEIoUzdOfyQsCgAmxaJRhBBCCLXq9kqLhMUAcEoFIYQQQj1JczMcNmY4EEIIoYGvz9eAdEazsYYD\nIYQQQvuTHsowx2nS7XW1sbG5fJbDk1MqmOFACCGEBq30YKIqquw3TbIuqv5u+XZPL5FcpcIw4EAI\nIYRQdhSDulMk2UsGHB5H9QQMOBBCCKGBIWFRr+tNEjaVeM7AZbEIIYQQylLCZl5DB81iOT7BwqJR\nhBBCCGVJNZk7OZL9opiExfJ8AmY4EEIIocGoKqrY3gs5NZt5nVLRLJrjE4x2NRx//6T+jeomryfw\nbWDAgRBCCPWBX71Zs67eW/eOhEkt6jDHQ6Si2SzXx7fvw/F5vSLynKdX/5Yw4EAIIYR6XPtJkBbd\njhseK0CTS048BBwJi+X6xfZ7qdTFzJF5Pk+v/i1hwIEQQgj1iK4rLTSbeV2tegAbo2gmy5EzMxwx\n3U5YdEhE8vTq3xIGHAghhFDP6jDy0G2WWjySZRFowrIBwFMFqDulktFptFYxS8OSyPdqDIABB0II\nIfStdBEupPcPzbihWcxrA9CE7fYpp16G0ByfmJHhqIuZ5WHZ00t/exhwIIQQQr3NccCgjucpFdMN\nOLxNqeT6+IwajtqYWdG78ymAAQdCCCHU+wzKHMfxmuHQLEY4zlvAYbNcn2AzhzoOALTotEmzo3Gj\nLNzbAQc2/kIIIYR6SWpWJbnFidc+5RbN9fGGzQA63S12l2KFZBIUeQAwqWMzJ8cnAIBhOwGR++cX\nuz+NKhwHc0bkHvjbOCCY4UAIIYR6m2ZTALC8lH/azDGpk+sTMipAmeM88VmD09qc49Z3tr/e2tFL\nsxnPQUAkAuHc14qb9uZGfeMerbzXMxwYcCCEEELdr+uFJ7rtAED79hhdSJiUcFyOLBi0TdFoVLEe\n+2zXFw0aAOxSrA27E/WK6d6lWdQv8gAg8cmJGNVkZx9aMDRH7v0pFQw4EEIIoX2y36YknWp5WDkC\nAJpFweOUSsJmfoHIYmYNx84WAwDe3toMAG/XNEs8qY8nA46ExfwiAQCJJ269iGLScQWBR84Y28tt\nRgEDDoQQQuhb2rhHu/rVzZ6GuBkOT1MqmsUCEpFIZsBRFzOH5crvbGthjvP21pbvjMmrVyz3roRJ\nAyIPALKQHKWYNCh1Wv/RozDgQAghhL6VuEkTbYOA/aZJWotGPQQcqkkDIi8LJKPxV23cmDsiJyCS\n3yzfFo2bpx1SsEsxAYA5zpOfN0wqCUAyw8EAQDFoSMaAAyGEEBqANIumZx2ymZTRbc+rVDSbBUQi\n8SQjw1EbMysi8rkTi8rC0gOnVpZHJJM6Md1+9NOGuEGvPLwcACSBc3dgUSwa6qMMBy6LRQghhL4V\nzXY8TY4AgGYxjuM8Fo2ygEjkTgKOQwr9qSOFAbFetf67uel384dLPAcAEiHuDiyqyUJS3+QaMMOB\nEEIIeZORw9As6il0AADNZhGJmJSl9z7vWsKifpGXhH01HP/Z2KjbbJdiVLRdclIaltZFlYRFxxQk\n94OVBWLajm4zmzmY4UAIIYQGJM1mNnMoc3iS7dIP3WYRn+ApL5KwWVAkMk9UygDAcZy71tTVNBtB\nWQi3LcsoDYlvbW6eUBzgueT5SIQzKVNMKgtEyPokuxdmOBBCCKH9a78HW4qW3DXeQ/Sg2zQiC/vN\ni9Q0G6nbCZP5RSILySmVhM0cx/nXhr0V7TpqlIakrU36xOJg6ogkEDfgCIl9k94ADDgQQgihb6Mq\nqrgBh+6lFYdmOTky375o9NWNjUvX73Zvb9id+PFL1bS1hahm06DI72vhZbCASCYW+9tvw1YSkgBg\nUsm+gEPmOYM6isn6aokK4JQKQgghlKX2uY3kRvM2hQPIcPh4sylzyPpd6pcNifMmFQHA21tbbObs\nVq3SkAQAqsnKwoLMc25r84TNAiJ/3TFD2285WxqSBMKll5FKAjFt1odrYgEzHAghhNC3pFsOtK50\nzZJmOxGZN+3MgGNnixGNm5sbdcdx3qlplgUS3dennAUlIgvEbW2uGHZQ4ivC0sg8X8aTjC3wXT97\niCzs+4qXec6k0LdTKt2W4WCMPfjgg7t3745EIkuWLOG4rmpSLMu69957FUUZPnz4D37wgz179lx7\n7bXFxcUAcM0111RUVHTXWSGEEBrMqqLKlLKQpyFxg5qUSXybf5B3vZDEzXB4Cjh0m0Zkv9uMK11t\nzJxUEli9vUWzQsBxM8pD0bg1rRRiur1xb+Lo4RG+dXt61WLBTha4ygKZP7LNZrASnywa7as1sdCN\nAccnn3wSDAYvv/zyd999t76+vqysrIsHf/DBB+Xl5eeff/5tt922Y8eOeDx+8sknL1q0qLtOBiGE\nEDowd39QO3N76IZjh3X
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,
2017-09-02 05:46:11 +00:00
"metadata": {
"collapsed": false
},
2017-02-22 23:59:43 +00:00
"outputs": [
{
"data": {
2017-09-02 05:46:11 +00:00
"image/png": "iVBORw0KGgoAAAANSUhEUgAAAsgAAAGpCAYAAACQ68AUAAAABHNCSVQICAgIfAhkiAAAAAlwSFlz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\ntRq0vBZrg+uAwckCci1
2017-02-22 23:59:43 +00:00
"text/plain": [
2017-09-02 05:46:11 +00:00
"<matplotlib.figure.Figure at 0x7f35c0c47ed0>"
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);"
]
}
],
"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
}
