"Fossies" - the Fresh Open Source Software Archive  

Source code changes of the file "notebooks/seasonality,_holiday_effects,_and_regressors.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.

seasonality,_holiday_effects,_and_regressors.ipynb  (prophet-0.7):seasonality,_holiday_effects,_and_regressors.ipynb  (prophet-1.0)
skipping to change at line 13 skipping to change at line 13
{ {
"cell_type": "code", "cell_type": "code",
"execution_count": 1, "execution_count": 1,
"metadata": { "metadata": {
"block_hidden": true "block_hidden": 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",
"from matplotlib import pyplot as plt\n", "from matplotlib import pyplot as plt\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\")\n", "warnings.filterwarnings(\"ignore\")"
]
},
{
"cell_type": "code",
"execution_count": 2,
"metadata": {
"block_hidden": true
},
"outputs": [
{
"name": "stderr",
"output_type": "stream",
"text": [
"INFO:numexpr.utils:NumExpr defaulting to 8 threads.\n"
]
}
],
"source": [
"df = pd.read_csv('../examples/example_wp_log_peyton_manning.csv')\n", "df = pd.read_csv('../examples/example_wp_log_peyton_manning.csv')\n",
"m = Prophet()\n", "m = Prophet()\n",
"m.fit(df)\n", "m.fit(df)\n",
"future = m.make_future_dataframe(periods=366)" "future = m.make_future_dataframe(periods=366)"
] ]
}, },
{ {
"cell_type": "code", "cell_type": "code",
"execution_count": 2, "execution_count": 2,
"metadata": { "metadata": {
"block_hidden": true "block_hidden": true
}, },
"outputs": [ "outputs": [
{ {
"name": "stderr", "name": "stderr",
"output_type": "stream", "output_type": "stream",
"text": [ "text": [
"WARNING:rpy2.rinterface_lib.callbacks:R[write to console]: Loading requir ed package: Rcpp\n", "R[write to console]: Loading required package: Rcpp\n",
"\n", "\n",
"WARNING:rpy2.rinterface_lib.callbacks:R[write to console]: Loading requir ed package: rlang\n", "R[write to console]: Loading required package: rlang\n",
"\n", "\n",
"WARNING:rpy2.rinterface_lib.callbacks:R[write to console]: Disabling dail y seasonality. Run prophet with daily.seasonality=TRUE to override this.\n", "R[write to console]: Disabling daily seasonality. Run prophet with daily. seasonality=TRUE to override this.\n",
"\n" "\n"
] ]
} }
], ],
"source": [ "source": [
"%%R\n", "%%R\n",
"library(prophet)\n", "library(prophet)\n",
"df <- read.csv('../examples/example_wp_log_peyton_manning.csv')\n", "df <- read.csv('../examples/example_wp_log_peyton_manning.csv')\n",
"m <- prophet(df)\n", "m <- prophet(df)\n",
"future <- make_future_dataframe(m, periods=366)" "future <- make_future_dataframe(m, periods=366)"
skipping to change at line 77 skipping to change at line 95
"cell_type": "code", "cell_type": "code",
"execution_count": 3, "execution_count": 3,
"metadata": { "metadata": {
"output_hidden": true "output_hidden": true
}, },
"outputs": [ "outputs": [
{ {
"name": "stderr", "name": "stderr",
"output_type": "stream", "output_type": "stream",
"text": [ "text": [
"WARNING:rpy2.rinterface_lib.callbacks:R[write to console]: \n", "R[write to console]: \n",
"Attaching package: ‘dplyr’\n", "Attaching package: ‘dplyr’\n",
"\n", "\n",
"\n", "\n",
"WARNING:rpy2.rinterface_lib.callbacks:R[write to console]: The following objects are masked from ‘package:stats’:\n", "R[write to console]: The following objects are masked from ‘package:stats ’:\n",
"\n", "\n",
" filter, lag\n", " filter, lag\n",
"\n", "\n",
"\n", "\n",
"WARNING:rpy2.rinterface_lib.callbacks:R[write to console]: The following objects are masked from ‘package:base’:\n", "R[write to console]: The following objects are masked from ‘package:base’ :\n",
"\n", "\n",
" intersect, setdiff, setequal, union\n", " intersect, setdiff, setequal, union\n",
"\n", "\n",
"\n" "\n"
] ]
} }
], ],
"source": [ "source": [
"%%R\n", "%%R\n",
"library(dplyr)\n", "library(dplyr)\n",
skipping to change at line 118 skipping to change at line 136
" holiday = 'superbowl',\n", " holiday = 'superbowl',\n",
" ds = as.Date(c('2010-02-07', '2014-02-02', '2016-02-07')),\n", " ds = as.Date(c('2010-02-07', '2014-02-02', '2016-02-07')),\n",
" lower_window = 0,\n", " lower_window = 0,\n",
" upper_window = 1\n", " upper_window = 1\n",
")\n", ")\n",
"holidays <- bind_rows(playoffs, superbowls)" "holidays <- bind_rows(playoffs, superbowls)"
] ]
}, },
{ {
"cell_type": "code", "cell_type": "code",
"execution_count": 4, "execution_count": 3,
"metadata": {}, "metadata": {},
"outputs": [], "outputs": [],
"source": [ "source": [
"playoffs = pd.DataFrame({\n", "playoffs = pd.DataFrame({\n",
" 'holiday': 'playoff',\n", " 'holiday': 'playoff',\n",
" 'ds': pd.to_datetime(['2008-01-13', '2009-01-03', '2010-01-16',\n", " 'ds': pd.to_datetime(['2008-01-13', '2009-01-03', '2010-01-16',\n",
" '2010-01-24', '2010-02-07', '2011-01-08',\n", " '2010-01-24', '2010-02-07', '2011-01-08',\n",
" '2013-01-12', '2014-01-12', '2014-01-19',\n", " '2013-01-12', '2014-01-12', '2014-01-19',\n",
" '2014-02-02', '2015-01-11', '2016-01-17',\n", " '2014-02-02', '2015-01-11', '2016-01-17',\n",
" '2016-01-24', '2016-02-07']),\n", " '2016-01-24', '2016-02-07']),\n",
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"cell_type": "markdown", "cell_type": "markdown",
"metadata": {}, "metadata": {},
"source": [ "source": [
"Above we have included the superbowl days as both playoff games and superbo wl games. This means that the superbowl effect will be an additional additive bo nus on top of the playoff effect.\n", "Above we have included the superbowl days as both playoff games and superbo wl games. This means that the superbowl effect will be an additional additive bo nus on top of the playoff effect.\n",
"\n", "\n",
"Once the table is created, holiday effects are included in the forecast by passing them in with the `holidays` argument. Here we do it with the Peyton Mann ing data from the [Quickstart](https://facebook.github.io/prophet/docs/quick_sta rt.html):" "Once the table is created, holiday effects are included in the forecast by passing them in with the `holidays` argument. Here we do it with the Peyton Mann ing data from the [Quickstart](https://facebook.github.io/prophet/docs/quick_sta rt.html):"
] ]
}, },
{ {
"cell_type": "code", "cell_type": "code",
"execution_count": 5, "execution_count": 4,
"metadata": { "metadata": {
"output_hidden": true "output_hidden": true
}, },
"outputs": [ "outputs": [
{ {
"name": "stderr", "name": "stderr",
"output_type": "stream", "output_type": "stream",
"text": [ "text": [
"WARNING:rpy2.rinterface_lib.callbacks:R[write to console]: Disabling dail y seasonality. Run prophet with daily.seasonality=TRUE to override this.\n", "R[write to console]: Disabling daily seasonality. Run prophet with daily. seasonality=TRUE to override this.\n",
"\n" "\n"
] ]
} }
], ],
"source": [ "source": [
"%%R\n", "%%R\n",
"m <- prophet(df, holidays = holidays)\n", "m <- prophet(df, holidays = holidays)\n",
"forecast <- predict(m, future)" "forecast <- predict(m, future)"
] ]
}, },
{ {
"cell_type": "code", "cell_type": "code",
"execution_count": 6, "execution_count": 4,
"metadata": {}, "metadata": {},
"outputs": [], "outputs": [],
"source": [ "source": [
"m = Prophet(holidays=holidays)\n", "m = Prophet(holidays=holidays)\n",
"forecast = m.fit(df).predict(future)" "forecast = m.fit(df).predict(future)"
] ]
}, },
{ {
"cell_type": "markdown", "cell_type": "markdown",
"metadata": {}, "metadata": {},
"source": [ "source": [
"The holiday effect can be seen in the `forecast` dataframe:" "The holiday effect can be seen in the `forecast` dataframe:"
] ]
}, },
{ {
"cell_type": "code", "cell_type": "code",
"execution_count": 7, "execution_count": 5,
"metadata": { "metadata": {
"output_hidden": true "output_hidden": true
}, },
"outputs": [ "outputs": [
{ {
"data": { "name": "stdout",
"text/plain": [ "output_type": "stream",
" ds playoff superbowl\n", "text": [
"17 2014-02-02 1.219683 1.201974\n", " ds playoff superbowl\n",
"18 2014-02-03 1.898971 1.460981\n", "17 2014-02-02 1.219646 1.211218\n",
"19 2015-01-11 1.219683 0.000000\n", "18 2014-02-03 1.913081 1.359259\n",
"20 2015-01-12 1.898971 0.000000\n", "19 2015-01-11 1.219646 0.000000\n",
"21 2016-01-17 1.219683 0.000000\n", "20 2015-01-12 1.913081 0.000000\n",
"22 2016-01-18 1.898971 0.000000\n", "21 2016-01-17 1.219646 0.000000\n",
"23 2016-01-24 1.219683 0.000000\n", "22 2016-01-18 1.913081 0.000000\n",
"24 2016-01-25 1.898971 0.000000\n", "23 2016-01-24 1.219646 0.000000\n",
"25 2016-02-07 1.219683 1.201974\n", "24 2016-01-25 1.913081 0.000000\n",
"26 2016-02-08 1.898971 1.460981\n" "25 2016-02-07 1.219646 1.211218\n",
] "26 2016-02-08 1.913081 1.359259\n"
}, ]
"metadata": {},
"output_type": "display_data"
} }
], ],
"source": [ "source": [
"%%R\n", "%%R\n",
"forecast %>% \n", "forecast %>% \n",
" select(ds, playoff, superbowl) %>% \n", " select(ds, playoff, superbowl) %>% \n",
" filter(abs(playoff + superbowl) > 0) %>%\n", " filter(abs(playoff + superbowl) > 0) %>%\n",
" tail(10)" " tail(10)"
] ]
}, },
{ {
"cell_type": "code", "cell_type": "code",
"execution_count": 8, "execution_count": 5,
"metadata": {}, "metadata": {},
"outputs": [ "outputs": [
{ {
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" vertical-align: middle;\n", " vertical-align: middle;\n",
" }\n", " }\n",
skipping to change at line 259 skipping to change at line 275
" <th></th>\n", " <th></th>\n",
" <th>ds</th>\n", " <th>ds</th>\n",
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"text/plain": [ "text/plain": [
" ds playoff superbowl\n", " ds playoff superbowl\n",
"2190 2014-02-02 1.217571 1.230312\n", "2190 2014-02-02 1.223965 1.201517\n",
"2191 2014-02-03 1.898042 1.466063\n", "2191 2014-02-03 1.901742 1.460471\n",
"2532 2015-01-11 1.217571 0.000000\n", "2532 2015-01-11 1.223965 0.000000\n",
"2533 2015-01-12 1.898042 0.000000\n", "2533 2015-01-12 1.901742 0.000000\n",
"2901 2016-01-17 1.217571 0.000000\n", "2901 2016-01-17 1.223965 0.000000\n",
"2902 2016-01-18 1.898042 0.000000\n", "2902 2016-01-18 1.901742 0.000000\n",
"2908 2016-01-24 1.217571 0.000000\n", "2908 2016-01-24 1.223965 0.000000\n",
"2909 2016-01-25 1.898042 0.000000\n", "2909 2016-01-25 1.901742 0.000000\n",
"2922 2016-02-07 1.217571 1.230312\n", "2922 2016-02-07 1.223965 1.201517\n",
"2923 2016-02-08 1.898042 1.466063" "2923 2016-02-08 1.901742 1.460471"
] ]
}, },
"execution_count": 8, "execution_count": 5,
"metadata": {}, "metadata": {},
"output_type": "execute_result" "output_type": "execute_result"
} }
], ],
"source": [ "source": [
"forecast[(forecast['playoff'] + forecast['superbowl']).abs() > 0][\n", "forecast[(forecast['playoff'] + forecast['superbowl']).abs() > 0][\n",
" ['ds', 'playoff', 'superbowl']][-10:]" " ['ds', 'playoff', 'superbowl']][-10:]"
] ]
}, },
{ {
"cell_type": "markdown", "cell_type": "markdown",
"metadata": {}, "metadata": {},
"source": [ "source": [
"The holiday effects will also show up in the components plot, where we see that there is a spike on the days around playoff appearances, with an especially large spike for the superbowl:" "The holiday effects will also show up in the components plot, where we see that there is a spike on the days around playoff appearances, with an especially large spike for the superbowl:"
] ]
}, },
{ {
"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 9 -h 12 -u in\n", "%%R -w 9 -h 12 -u in\n",
"prophet_plot_components(m, forecast)" "prophet_plot_components(m, forecast)"
] ]
}, },
{ {
"cell_type": "code", "cell_type": "code",
"execution_count": 10, "execution_count": 6,
"metadata": {}, "metadata": {},
"outputs": [ "outputs": [
{ {
"data": { "data": {
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"text/plain": [ "text/plain": [
"<Figure size 648x864 with 4 Axes>" "<Figure size 648x864 with 4 Axes>"
] ]
}, },
"metadata": {}, "metadata": {},
"output_type": "display_data" "output_type": "display_data"
} }
], ],
"source": [ "source": [
"fig = m.plot_components(forecast)" "fig = m.plot_components(forecast)"
] ]
}, },
{ {
"cell_type": "markdown", "cell_type": "markdown",
"metadata": {}, "metadata": {},
"source": [ "source": [
"Individual holidays can be plotted using the `plot_forecast_component` func tion (imported from `fbprophet.plot` in Python) like `plot_forecast_component(m, forecast, 'superbowl')` to plot just the superbowl holiday component." "Individual holidays can be plotted using the `plot_forecast_component` func tion (imported from `prophet.plot` in Python) like `plot_forecast_component(m, f orecast, 'superbowl')` to plot just the superbowl holiday component."
] ]
}, },
{ {
"cell_type": "markdown", "cell_type": "markdown",
"metadata": {}, "metadata": {},
"source": [ "source": [
"### Built-in Country Holidays\n", "### Built-in Country Holidays\n",
"\n", "\n",
"You can use a built-in collection of country-specific holidays using the `a dd_country_holidays` method (Python) or function (R). The name of the country is specified, and then major holidays for that country will be included in additio n to any holidays that are specified via the `holidays` argument described above :" "You can use a built-in collection of country-specific holidays using the `a dd_country_holidays` method (Python) or function (R). The name of the country is specified, and then major holidays for that country will be included in additio n to any holidays that are specified via the `holidays` argument described above :"
] ]
}, },
{ {
"cell_type": "code", "cell_type": "code",
"execution_count": 11, "execution_count": 7,
"metadata": { "metadata": {
"output_hidden": true "output_hidden": true
}, },
"outputs": [ "outputs": [
{ {
"name": "stderr", "name": "stderr",
"output_type": "stream", "output_type": "stream",
"text": [ "text": [
"WARNING:rpy2.rinterface_lib.callbacks:R[write to console]: Disabling dail y seasonality. Run prophet with daily.seasonality=TRUE to override this.\n", "R[write to console]: Disabling daily seasonality. Run prophet with daily. seasonality=TRUE to override this.\n",
"\n" "\n"
] ]
} }
], ],
"source": [ "source": [
"%%R\n", "%%R\n",
"m <- prophet(holidays = holidays)\n", "m <- prophet(holidays = holidays)\n",
"m <- add_country_holidays(m, country_name = 'US')\n", "m <- add_country_holidays(m, country_name = 'US')\n",
"m <- fit.prophet(m, df)" "m <- fit.prophet(m, df)"
] ]
}, },
{ {
"cell_type": "code", "cell_type": "code",
"execution_count": 12, "execution_count": 7,
"metadata": { "metadata": {
"output_hidden": true "output_hidden": true
}, },
"outputs": [ "outputs": [
{ {
"data": { "data": {
"text/plain": [ "text/plain": [
"<fbprophet.forecaster.Prophet at 0x7fa913d94780>" "<prophet.forecaster.Prophet at 0x7f2a61da1850>"
] ]
}, },
"execution_count": 12, "execution_count": 7,
"metadata": {}, "metadata": {},
"output_type": "execute_result" "output_type": "execute_result"
} }
], ],
"source": [ "source": [
"m = Prophet(holidays=holidays)\n", "m = Prophet(holidays=holidays)\n",
"m.add_country_holidays(country_name='US')\n", "m.add_country_holidays(country_name='US')\n",
"m.fit(df)" "m.fit(df)"
] ]
}, },
{ {
"cell_type": "markdown", "cell_type": "markdown",
"metadata": {}, "metadata": {},
"source": [ "source": [
"You can see which holidays were included by looking at the `train_holiday_n ames` (Python) or `train.holiday.names` (R) attribute of the model:" "You can see which holidays were included by looking at the `train_holiday_n ames` (Python) or `train.holiday.names` (R) attribute of the model:"
] ]
}, },
{ {
"cell_type": "code", "cell_type": "code",
"execution_count": 13, "execution_count": 8,
"metadata": {}, "metadata": {},
"outputs": [ "outputs": [
{ {
"data": { "name": "stdout",
"text/plain": [ "output_type": "stream",
" [1] \"playoff\" \"superbowl\" \n", "text": [
" [3] \"New Year's Day\" \"Martin Luther King, Jr. Day\"\n", " [1] \"playoff\" \"superbowl\" \n",
" [5] \"Washington's Birthday\" \"Memorial Day\" \n", " [3] \"New Year's Day\" \"Martin Luther King Jr. Day\" \n",
" [7] \"Independence Day\" \"Labor Day\" \n", " [5] \"Washington's Birthday\" \"Memorial Day\" \n",
" [9] \"Columbus Day\" \"Veterans Day\" \n", " [7] \"Independence Day\" \"Labor Day\" \n",
"[11] \"Veterans Day (Observed)\" \"Thanksgiving\" \n", " [9] \"Columbus Day\" \"Veterans Day\" \n",
"[13] \"Christmas Day\" \"Independence Day (Observed)\"\n", "[11] \"Veterans Day (Observed)\" \"Thanksgiving\" \n",
"[15] \"Christmas Day (Observed)\" \"New Year's Day (Observed)\" \n" "[13] \"Christmas Day\" \"Independence Day (Observed)\"\n",
] "[15] \"Christmas Day (Observed)\" \"New Year's Day (Observed)\" \n"
}, ]
"metadata": {},
"output_type": "display_data"
} }
], ],
"source": [ "source": [
"%%R\n", "%%R\n",
"m$train.holiday.names" "m$train.holiday.names"
] ]
}, },
{ {
"cell_type": "code", "cell_type": "code",
"execution_count": 14, "execution_count": 8,
"metadata": {}, "metadata": {},
"outputs": [ "outputs": [
{ {
"data": { "data": {
"text/plain": [ "text/plain": [
"0 playoff\n", "0 playoff\n",
"1 superbowl\n", "1 superbowl\n",
"2 New Year's Day\n", "2 New Year's Day\n",
"3 Martin Luther King, Jr. Day\n", "3 Martin Luther King Jr. Day\n",
"4 Washington's Birthday\n", "4 Washington's Birthday\n",
"5 Memorial Day\n", "5 Memorial Day\n",
"6 Independence Day\n", "6 Independence Day\n",
"7 Labor Day\n", "7 Labor Day\n",
"8 Columbus Day\n", "8 Columbus Day\n",
"9 Veterans Day\n", "9 Veterans Day\n",
"10 Thanksgiving\n", "10 Thanksgiving\n",
"11 Christmas Day\n", "11 Christmas Day\n",
"12 Christmas Day (Observed)\n", "12 Christmas Day (Observed)\n",
"13 Veterans Day (Observed)\n", "13 Veterans Day (Observed)\n",
"14 Independence Day (Observed)\n", "14 Independence Day (Observed)\n",
"15 New Year's Day (Observed)\n", "15 New Year's Day (Observed)\n",
"dtype: object" "dtype: object"
] ]
}, },
"execution_count": 14, "execution_count": 8,
"metadata": {}, "metadata": {},
"output_type": "execute_result" "output_type": "execute_result"
} }
], ],
"source": [ "source": [
"m.train_holiday_names" "m.train_holiday_names"
] ]
}, },
{ {
"cell_type": "markdown", "cell_type": "markdown",
skipping to change at line 537 skipping to change at line 551
"source": [ "source": [
"The holidays for each country are provided by the `holidays` package in Pyt hon. A list of available countries, and the country name to use, is available on their page: https://github.com/dr-prodigy/python-holidays. In addition to those countries, Prophet includes holidays for these countries: Brazil (BR), Indonesi a (ID), India (IN), Malaysia (MY), Vietnam (VN), Thailand (TH), Philippines (PH) , Pakistan (PK), Bangladesh (BD), Egypt (EG), China (CN), and Russian (RU), Kore a (KR), Belarus (BY), and United Arab Emirates (AE).\n", "The holidays for each country are provided by the `holidays` package in Pyt hon. A list of available countries, and the country name to use, is available on their page: https://github.com/dr-prodigy/python-holidays. In addition to those countries, Prophet includes holidays for these countries: Brazil (BR), Indonesi a (ID), India (IN), Malaysia (MY), Vietnam (VN), Thailand (TH), Philippines (PH) , Pakistan (PK), Bangladesh (BD), Egypt (EG), China (CN), and Russian (RU), Kore a (KR), Belarus (BY), and United Arab Emirates (AE).\n",
"\n", "\n",
"In Python, most holidays are computed deterministically and so are availabl e for any date range; a warning will be raised if dates fall outside the range s upported by that country. In R, holiday dates are computed for 1995 through 2044 and stored in the package as `data-raw/generated_holidays.csv`. If a wider date range is needed, this script can be used to replace that file with a different date range: https://github.com/facebook/prophet/blob/master/python/scripts/gener ate_holidays_file.py.\n", "In Python, most holidays are computed deterministically and so are availabl e for any date range; a warning will be raised if dates fall outside the range s upported by that country. In R, holiday dates are computed for 1995 through 2044 and stored in the package as `data-raw/generated_holidays.csv`. If a wider date range is needed, this script can be used to replace that file with a different date range: https://github.com/facebook/prophet/blob/master/python/scripts/gener ate_holidays_file.py.\n",
"\n", "\n",
"As above, the country-level holidays will then show up in the components pl ot:" "As above, the country-level holidays will then show up in the components pl ot:"
] ]
}, },
{ {
"cell_type": "code", "cell_type": "code",
"execution_count": 15, "execution_count": 9,
"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 9 -h 12 -u in\n", "%%R -w 9 -h 12 -u in\n",
"forecast <- predict(m, future)\n", "forecast <- predict(m, future)\n",
"prophet_plot_components(m, forecast)" "prophet_plot_components(m, forecast)"
] ]
}, },
{ {
"cell_type": "code", "cell_type": "code",
"execution_count": 16, "execution_count": 9,
"metadata": {}, "metadata": {},
"outputs": [ "outputs": [
{ {
"data": { "data": {
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"text/plain": [ "text/plain": [
"<Figure size 648x864 with 4 Axes>" "<Figure size 648x864 with 4 Axes>"
] ]
}, },
"metadata": {}, "metadata": {},
"output_type": "display_data" "output_type": "display_data"
} }
], ],
"source": [ "source": [
"forecast = m.predict(future)\n", "forecast = m.predict(future)\n",
"fig = m.plot_components(forecast)" "fig = m.plot_components(forecast)"
] ]
}, },
{ {
"cell_type": "markdown", "cell_type": "markdown",
"metadata": {}, "metadata": {},
"source": [ "source": [
"### Fourier Order for Seasonalities\n", "### Fourier Order for Seasonalities\n",
"\n", "\n",
"Seasonalities are estimated using a partial Fourier sum. See [the paper](ht tps://peerj.com/preprints/3190/) for complete details, and [this figure on Wikip edia](https://en.wikipedia.org/wiki/Fourier_series#/media/File:Fourier_Series.sv g) for an illustration of how a partial Fourier sum can approximate an aribtrary periodic signal. The number of terms in the partial sum (the order) is a parame ter that determines how quickly the seasonality can change. To illustrate this, consider the Peyton Manning data from the [Quickstart](https://facebook.github.i o/prophet/docs/quick_start.html). The default Fourier order for yearly seasonali ty is 10, which produces this fit:" "Seasonalities are estimated using a partial Fourier sum. See [the paper](ht tps://peerj.com/preprints/3190/) for complete details, and [this figure on Wikip edia](https://en.wikipedia.org/wiki/Fourier_series#/media/File:Fourier_Series.sv g) for an illustration of how a partial Fourier sum can approximate an arbitrary periodic signal. The number of terms in the partial sum (the order) is a parame ter that determines how quickly the seasonality can change. To illustrate this, consider the Peyton Manning data from the [Quickstart](https://facebook.github.i o/prophet/docs/quick_start.html). The default Fourier order for yearly seasonali ty is 10, which produces this fit:"
] ]
}, },
{ {
"cell_type": "code", "cell_type": "code",
"execution_count": 17, "execution_count": 10,
"metadata": { "metadata": {
"output_hidden": true "output_hidden": true
}, },
"outputs": [ "outputs": [
{ {
"name": "stderr", "name": "stderr",
"output_type": "stream", "output_type": "stream",
"text": [ "text": [
"WARNING:rpy2.rinterface_lib.callbacks:R[write to console]: Disabling dail y seasonality. Run prophet with daily.seasonality=TRUE to override this.\n", "R[write to console]: Disabling daily seasonality. Run prophet with daily. seasonality=TRUE to override this.\n",
"\n" "\n"
] ]
}, },
{ {
"data": { "data": {
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}, },
"metadata": {}, "metadata": {},
"output_type": "display_data" "output_type": "display_data"
} }
], ],
"source": [ "source": [
"%%R -w 9 -h 3 -u in\n", "%%R -w 9 -h 3 -u in\n",
"m <- prophet(df)\n", "m <- prophet(df)\n",
"prophet:::plot_yearly(m)" "prophet:::plot_yearly(m)"
] ]
}, },
{ {
"cell_type": "code", "cell_type": "code",
"execution_count": 18, "execution_count": 10,
"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": [
"from fbprophet.plot import plot_yearly\n", "from prophet.plot import plot_yearly\n",
"m = Prophet().fit(df)\n", "m = Prophet().fit(df)\n",
"a = plot_yearly(m)" "a = plot_yearly(m)"
] ]
}, },
{ {
"cell_type": "markdown", "cell_type": "markdown",
"metadata": {}, "metadata": {},
"source": [ "source": [
"The default values are often appropriate, but they can be increased when th e seasonality needs to fit higher-frequency changes, and generally be less smoot h. The Fourier order can be specified for each built-in seasonality when instant iating the model, here it is increased to 20:" "The default values are often appropriate, but they can be increased when th e seasonality needs to fit higher-frequency changes, and generally be less smoot h. The Fourier order can be specified for each built-in seasonality when instant iating the model, here it is increased to 20:"
] ]
}, },
{ {
"cell_type": "code", "cell_type": "code",
"execution_count": 19, "execution_count": 11,
"metadata": { "metadata": {
"output_hidden": true "output_hidden": true
}, },
"outputs": [ "outputs": [
{ {
"name": "stderr", "name": "stderr",
"output_type": "stream", "output_type": "stream",
"text": [ "text": [
"WARNING:rpy2.rinterface_lib.callbacks:R[write to console]: Disabling dail y seasonality. Run prophet with daily.seasonality=TRUE to override this.\n", "R[write to console]: Disabling daily seasonality. Run prophet with daily. seasonality=TRUE to override this.\n",
"\n" "\n"
] ]
}, },
{ {
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}, },
"metadata": {}, "metadata": {},
"output_type": "display_data" "output_type": "display_data"
} }
], ],
"source": [ "source": [
"%%R -w 9 -h 3 -u in\n", "%%R -w 9 -h 3 -u in\n",
"m <- prophet(df, yearly.seasonality = 20)\n", "m <- prophet(df, yearly.seasonality = 20)\n",
"prophet:::plot_yearly(m)" "prophet:::plot_yearly(m)"
] ]
}, },
{ {
"cell_type": "code", "cell_type": "code",
"execution_count": 20, "execution_count": 11,
"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": [
"from fbprophet.plot import plot_yearly\n", "from prophet.plot import plot_yearly\n",
"m = Prophet(yearly_seasonality=20).fit(df)\n", "m = Prophet(yearly_seasonality=20).fit(df)\n",
"a = plot_yearly(m)" "a = plot_yearly(m)"
] ]
}, },
{ {
"cell_type": "markdown", "cell_type": "markdown",
"metadata": {}, "metadata": {},
"source": [ "source": [
"Increasing the number of Fourier terms allows the seasonality to fit faster changing cycles, but can also lead to overfitting: N Fourier terms corresponds to 2N variables used for modeling the cycle\n", "Increasing the number of Fourier terms allows the seasonality to fit faster changing cycles, but can also lead to overfitting: N Fourier terms corresponds to 2N variables used for modeling the cycle\n",
"\n", "\n",
skipping to change at line 712 skipping to change at line 726
"\n", "\n",
"Prophet will by default fit weekly and yearly seasonalities, if the time se ries is more than two cycles long. It will also fit daily seasonality for a sub- daily time series. You can add other seasonalities (monthly, quarterly, hourly) using the `add_seasonality` method (Python) or function (R).\n", "Prophet will by default fit weekly and yearly seasonalities, if the time se ries is more than two cycles long. It will also fit daily seasonality for a sub- daily time series. You can add other seasonalities (monthly, quarterly, hourly) using the `add_seasonality` method (Python) or function (R).\n",
"\n", "\n",
"The inputs to this function are a name, the period of the seasonality in da ys, and the Fourier order for the seasonality. For reference, by default Prophet uses a Fourier order of 3 for weekly seasonality and 10 for yearly seasonality. An optional input to `add_seasonality` is the prior scale for that seasonal com ponent - this is discussed below.\n", "The inputs to this function are a name, the period of the seasonality in da ys, and the Fourier order for the seasonality. For reference, by default Prophet uses a Fourier order of 3 for weekly seasonality and 10 for yearly seasonality. An optional input to `add_seasonality` is the prior scale for that seasonal com ponent - this is discussed below.\n",
"\n", "\n",
"As an example, here we fit the Peyton Manning data from the [Quickstart](ht tps://facebook.github.io/prophet/docs/quick_start.html), but replace the weekly seasonality with monthly seasonality. The monthly seasonality then will appear i n the components plot:" "As an example, here we fit the Peyton Manning data from the [Quickstart](ht tps://facebook.github.io/prophet/docs/quick_start.html), but replace the weekly seasonality with monthly seasonality. The monthly seasonality then will appear i n the components plot:"
] ]
}, },
{ {
"cell_type": "code", "cell_type": "code",
"execution_count": 21, "execution_count": 12,
"metadata": { "metadata": {
"output_hidden": true "output_hidden": true
}, },
"outputs": [ "outputs": [
{ {
"name": "stderr", "name": "stderr",
"output_type": "stream", "output_type": "stream",
"text": [ "text": [
"WARNING:rpy2.rinterface_lib.callbacks:R[write to console]: Disabling dail y seasonality. Run prophet with daily.seasonality=TRUE to override this.\n", "R[write to console]: Disabling daily seasonality. Run prophet with daily. seasonality=TRUE to override this.\n",
"\n" "\n"
] ]
}, },
{ {
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