"Fossies" - the Fresh Open Source Software Archive  

Source code changes of the file "notebooks/outliers.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.

outliers.ipynb  (prophet-0.7):outliers.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
"collapsed": true
}, },
"outputs": [], "outputs": [],
"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",
"logging.getLogger('numexpr').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
"collapsed": true
}, },
"outputs": [], "outputs": [
{
"name": "stderr",
"output_type": "stream",
"text": [
"R[write to console]: Loading required package: Rcpp\n",
"\n",
"R[write to console]: Loading required package: rlang\n",
"\n"
]
}
],
"source": [ "source": [
"%%R\n", "%%R\n",
"library(prophet)" "library(prophet)"
] ]
}, },
{ {
"cell_type": "markdown", "cell_type": "markdown",
"metadata": {}, "metadata": {},
"source": [ "source": [
"There are two main ways that outliers can affect Prophet forecasts. Here we make a forecast on the logged Wikipedia visits to the R page from before, but w ith a block of bad data:" "There are two main ways that outliers can affect Prophet forecasts. Here we make a forecast on the logged Wikipedia visits to the R page from before, but w ith a block of bad data:"
] ]
}, },
{ {
"cell_type": "code", "cell_type": "code",
"execution_count": 3, "execution_count": 3,
"metadata": { "metadata": {
"output_hidden": true "output_hidden": true
}, },
"outputs": [ "outputs": [
{ {
"data": { "name": "stderr",
"text/plain": [ "output_type": "stream",
"Initial log joint probability = -28.5336\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 <- read.csv('../examples/example_wp_log_R_outliers1.csv')\n", "df <- read.csv('../examples/example_wp_log_R_outliers1.csv')\n",
"m <- prophet(df)\n", "m <- prophet(df)\n",
"future <- make_future_dataframe(m, periods = 1096)\n", "future <- make_future_dataframe(m, periods = 1096)\n",
"forecast <- predict(m, future)\n", "forecast <- predict(m, future)\n",
"plot(m, forecast)" "plot(m, forecast)"
] ]
}, },
{ {
"cell_type": "code", "cell_type": "code",
"execution_count": 4, "execution_count": 2,
"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 = pd.read_csv('../examples/example_wp_log_R_outliers1.csv')\n", "df = pd.read_csv('../examples/example_wp_log_R_outliers1.csv')\n",
skipping to change at line 113 skipping to change at line 120
"cell_type": "markdown", "cell_type": "markdown",
"metadata": {}, "metadata": {},
"source": [ "source": [
"The trend forecast seems reasonable, but the uncertainty intervals seem way too wide. Prophet is able to handle the outliers in the history, but only by fi tting them with trend changes. The uncertainty model then expects future trend c hanges of similar magnitude.\n", "The trend forecast seems reasonable, but the uncertainty intervals seem way too wide. Prophet is able to handle the outliers in the history, but only by fi tting them with trend changes. The uncertainty model then expects future trend c hanges of similar magnitude.\n",
"\n", "\n",
"The best way to handle outliers is to remove them - Prophet has no problem with missing data. If you set their values to `NA` in the history but leave the dates in `future`, then Prophet will give you a prediction for their values." "The best way to handle outliers is to remove them - Prophet has no problem with missing data. If you set their values to `NA` in the history but leave the dates in `future`, then Prophet will give you a prediction for their values."
] ]
}, },
{ {
"cell_type": "code", "cell_type": "code",
"execution_count": 5, "execution_count": 4,
"metadata": { "metadata": {
"output_hidden": true "output_hidden": true
}, },
"outputs": [ "outputs": [
{ {
"data": { "name": "stderr",
"text/plain": [ "output_type": "stream",
"Initial log joint probability = -21.2638\n", "text": [
"Error evaluating model log probability: Non-finite gradient.\n", "R[write to console]: Disabling daily seasonality. Run prophet with daily.
"Error evaluating model log probability: Non-finite gradient.\n", seasonality=TRUE to override this.\n",
"\n", "\n"
"Optimization terminated normally: \n", ]
" Convergence detected: relative gradient magnitude is below tolerance\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",
"outliers <- (as.Date(df$ds) > as.Date('2010-01-01')\n", "outliers <- (as.Date(df$ds) > as.Date('2010-01-01')\n",
" & as.Date(df$ds) < as.Date('2011-01-01'))\n", " & as.Date(df$ds) < as.Date('2011-01-01'))\n",
"df$y[outliers] = NA\n", "df$y[outliers] = NA\n",
"m <- prophet(df)\n", "m <- prophet(df)\n",
"forecast <- predict(m, future)\n", "forecast <- predict(m, future)\n",
"plot(m, forecast)" "plot(m, forecast)"
] ]
}, },
{ {
"cell_type": "code", "cell_type": "code",
"execution_count": 6, "execution_count": 3,
"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": [
"df.loc[(df['ds'] > '2010-01-01') & (df['ds'] < '2011-01-01'), 'y'] = None\n ", "df.loc[(df['ds'] > '2010-01-01') & (df['ds'] < '2011-01-01'), 'y'] = None\n ",
skipping to change at line 181 skipping to change at line 182
}, },
{ {
"cell_type": "markdown", "cell_type": "markdown",
"metadata": {}, "metadata": {},
"source": [ "source": [
"In the above example the outliers messed up the uncertainty estimation but did not impact the main forecast `yhat`. This isn't always the case, as in this example with added outliers:" "In the above example the outliers messed up the uncertainty estimation but did not impact the main forecast `yhat`. This isn't always the case, as in this example with added outliers:"
] ]
}, },
{ {
"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 = -27.5907\n", "text": [
"Error evaluating model log probability: Non-finite gradient.\n", "R[write to console]: Disabling daily seasonality. Run prophet with daily.
"Error evaluating model log probability: Non-finite gradient.\n", seasonality=TRUE to override this.\n",
"\n", "\n"
"Optimization terminated normally: \n", ]
" Convergence detected: relative gradient magnitude is below tolerance\n
"
]
},
"metadata": {},
"output_type": "display_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 <- read.csv('../examples/example_wp_log_R_outliers2.csv')\n", "df <- read.csv('../examples/example_wp_log_R_outliers2.csv')\n",
"m <- prophet(df)\n", "m <- prophet(df)\n",
"future <- make_future_dataframe(m, periods = 1096)\n", "future <- make_future_dataframe(m, periods = 1096)\n",
"forecast <- predict(m, future)\n", "forecast <- predict(m, future)\n",
"plot(m, forecast)" "plot(m, forecast)"
] ]
}, },
{ {
"cell_type": "code", "cell_type": "code",
"execution_count": 8, "execution_count": 4,
"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": [
"df = pd.read_csv('../examples/example_wp_log_R_outliers2.csv')\n", "df = pd.read_csv('../examples/example_wp_log_R_outliers2.csv')\n",
skipping to change at line 251 skipping to change at line 246
}, },
{ {
"cell_type": "markdown", "cell_type": "markdown",
"metadata": {}, "metadata": {},
"source": [ "source": [
"Here a group of extreme outliers in June 2015 mess up the seasonality estim ate, so their effect reverberates into the future forever. Again the right appro ach is to remove them:" "Here a group of extreme outliers in June 2015 mess up the seasonality estim ate, so their effect reverberates into the future forever. Again the right appro ach is to remove them:"
] ]
}, },
{ {
"cell_type": "code", "cell_type": "code",
"execution_count": 9, "execution_count": 6,
"metadata": { "metadata": {
"output_hidden": true "output_hidden": true
}, },
"outputs": [ "outputs": [
{ {
"data": { "name": "stderr",
"text/plain": [ "output_type": "stream",
"Initial log joint probability = -24.7625\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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