"Fossies" - the Fresh Open Source Software Archive  

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

multiplicative_seasonality.ipynb  (prophet-0.7):multiplicative_seasonality.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 numpy as np\n", "import numpy as np\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",
"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": [
"By default Prophet fits additive seasonalities, meaning the effect of the s easonality is added to the trend to get the forecast. This time series of the nu mber of air passengers is an example of when additive seasonality does not work: " "By default Prophet fits additive seasonalities, meaning the effect of the s easonality is added to the trend to get the forecast. This time series of the nu mber of air passengers is an example of when additive seasonality does not work: "
] ]
}, },
{ {
"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 = -2.46502\n", "text": [
"Optimization terminated normally: \n", "R[write to console]: Disabling weekly seasonality. Run prophet with weekl
" Convergence detected: absolute parameter change was below tolerance\n" y.seasonality=TRUE to override this.\n",
] "\n",
}, "R[write to console]: Disabling daily seasonality. Run prophet with daily.
"metadata": {}, seasonality=TRUE to override this.\n",
"output_type": "display_data" "\n"
]
}, },
{ {
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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_air_passengers.csv')\n", "df <- read.csv('../examples/example_air_passengers.csv')\n",
"m <- prophet(df)\n", "m <- prophet(df)\n",
"future <- make_future_dataframe(m, 50, freq = 'm')\n", "future <- make_future_dataframe(m, 50, freq = 'm')\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": [
{ {
"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_air_passengers.csv')\n", "df = pd.read_csv('../examples/example_air_passengers.csv')\n",
skipping to change at line 115 skipping to change at line 124
"cell_type": "markdown", "cell_type": "markdown",
"metadata": {}, "metadata": {},
"source": [ "source": [
"This time series has a clear yearly cycle, but the seasonality in the forec ast is too large at the start of the time series and too small at the end. In th is time series, the seasonality is not a constant additive factor as assumed by Prophet, rather it grows with the trend. This is multiplicative seasonality.\n", "This time series has a clear yearly cycle, but the seasonality in the forec ast is too large at the start of the time series and too small at the end. In th is time series, the seasonality is not a constant additive factor as assumed by Prophet, rather it grows with the trend. This is multiplicative seasonality.\n",
"\n", "\n",
"Prophet can model multiplicative seasonality by setting `seasonality_mode=' multiplicative'` in the input arguments:" "Prophet can model multiplicative seasonality by setting `seasonality_mode=' multiplicative'` in the input arguments:"
] ]
}, },
{ {
"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 = -2.46502\n", "text": [
"Optimization terminated normally: \n", "R[write to console]: Disabling weekly seasonality. Run prophet with weekl
" Convergence detected: relative gradient magnitude is below tolerance\n y.seasonality=TRUE to override this.\n",
" "\n",
] "R[write to console]: Disabling daily seasonality. Run prophet with daily.
}, seasonality=TRUE to override this.\n",
"metadata": {}, "\n"
"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",
"m <- prophet(df, seasonality.mode = 'multiplicative')\n", "m <- prophet(df, seasonality.mode = 'multiplicative')\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": [
"m = Prophet(seasonality_mode='multiplicative')\n", "m = Prophet(seasonality_mode='multiplicative')\n",
skipping to change at line 178 skipping to change at line 186
}, },
{ {
"cell_type": "markdown", "cell_type": "markdown",
"metadata": {}, "metadata": {},
"source": [ "source": [
"The components figure will now show the seasonality as a percent of the tre nd:" "The components figure will now show the seasonality as a percent of the tre nd:"
] ]
}, },
{ {
"cell_type": "code", "cell_type": "code",
"execution_count": 7, "execution_count": 5,
"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 6 -u in\n", "%%R -w 9 -h 6 -u in\n",
"prophet_plot_components(m, forecast)" "prophet_plot_components(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 648x432 with 2 Axes>" "<Figure size 648x432 with 2 Axes>"
] ]
}, },
"metadata": {}, "metadata": {},
"output_type": "display_data" "output_type": "display_data"
} }
], ],
"source": [ "source": [
"fig = m.plot_components(forecast)" "fig = m.plot_components(forecast)"
skipping to change at line 227 skipping to change at line 235
"cell_type": "markdown", "cell_type": "markdown",
"metadata": {}, "metadata": {},
"source": [ "source": [
"With `seasonality_mode='multiplicative'`, holiday effects will also be mode led as multiplicative. Any added seasonalities or extra regressors will by defau lt use whatever `seasonality_mode` is set to, but can be overriden by specifying `mode='additive'` or `mode='multiplicative'` as an argument when adding the sea sonality or regressor.\n", "With `seasonality_mode='multiplicative'`, holiday effects will also be mode led as multiplicative. Any added seasonalities or extra regressors will by defau lt use whatever `seasonality_mode` is set to, but can be overriden by specifying `mode='additive'` or `mode='multiplicative'` as an argument when adding the sea sonality or regressor.\n",
"\n", "\n",
"For example, this block sets the built-in seasonalities to multiplicative, but includes an additive quarterly seasonality and an additive regressor:" "For example, this block sets the built-in seasonalities to multiplicative, but includes an additive quarterly seasonality and an additive regressor:"
] ]
}, },
{ {
"cell_type": "code", "cell_type": "code",
"execution_count": 9, "execution_count": 6,
"metadata": { "metadata": {},
"collapsed": true
},
"outputs": [], "outputs": [],
"source": [ "source": [
"%%R\n", "%%R\n",
"m <- prophet(seasonality.mode = 'multiplicative')\n", "m <- prophet(seasonality.mode = 'multiplicative')\n",
"m <- add_seasonality(m, 'quarterly', period = 91.25, fourier.order = 8, mod e = 'additive')\n", "m <- add_seasonality(m, 'quarterly', period = 91.25, fourier.order = 8, mod e = 'additive')\n",
"m <- add_regressor(m, 'regressor', mode = 'additive')" "m <- add_regressor(m, 'regressor', mode = 'additive')"
] ]
}, },
{ {
"cell_type": "code", "cell_type": "code",
"execution_count": 10, "execution_count": 5,
"metadata": { "metadata": {
"output_hidden": true "output_hidden": true
}, },
"outputs": [ "outputs": [
{ {
"data": { "data": {
"text/plain": [ "text/plain": [
"<fbprophet.forecaster.Prophet at 0x7f2f9db52d10>" "<prophet.forecaster.Prophet at 0x7fd258219d30>"
] ]
}, },
"execution_count": 10, "execution_count": 5,
"metadata": {}, "metadata": {},
"output_type": "execute_result" "output_type": "execute_result"
} }
], ],
"source": [ "source": [
"m = Prophet(seasonality_mode='multiplicative')\n", "m = Prophet(seasonality_mode='multiplicative')\n",
"m.add_seasonality('quarterly', period=91.25, fourier_order=8, mode='additiv e')\n", "m.add_seasonality('quarterly', period=91.25, fourier_order=8, mode='additiv e')\n",
"m.add_regressor('regressor', mode='additive')" "m.add_regressor('regressor', mode='additive')"
] ]
}, },
skipping to change at line 287 skipping to change at line 293
"language_info": { "language_info": {
"codemirror_mode": { "codemirror_mode": {
"name": "ipython", "name": "ipython",
"version": 3 "version": 3
}, },
"file_extension": ".py", "file_extension": ".py",
"mimetype": "text/x-python", "mimetype": "text/x-python",
"name": "python", "name": "python",
"nbconvert_exporter": "python", "nbconvert_exporter": "python",
"pygments_lexer": "ipython3", "pygments_lexer": "ipython3",
"version": "3.7.8" "version": "3.8.3"
} }
}, },
"nbformat": 4, "nbformat": 4,
"nbformat_minor": 1 "nbformat_minor": 1
} }
 End of changes. 23 change blocks. 
45 lines changed or deleted 54 lines changed or added

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