"Fossies" - the Fresh Open Source Software Archive  

Source code changes of the file "notebooks/non-daily_data.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.

non-daily_data.ipynb  (prophet-0.7):non-daily_data.ipynb  (prophet-1.0)
{ {
"cells": [ "cells": [
{ {
"cell_type": "code", "cell_type": "code",
"execution_count": 2, "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",
"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
}, },
skipping to change at line 76 skipping to change at line 77
] ]
}, },
{ {
"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 = -1444.46\n", "text": [
"Optimization terminated normally: \n", "R[write to console]: Disabling yearly seasonality. Run prophet with yearl
" Convergence detected: relative gradient magnitude is below tolerance\n y.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_yosemite_temps.csv')\n", "df <- read.csv('../examples/example_yosemite_temps.csv')\n",
"m <- prophet(df, changepoint.prior.scale=0.01)\n", "m <- prophet(df, changepoint.prior.scale=0.01)\n",
"future <- make_future_dataframe(m, periods = 300, freq = 60 * 60)\n", "future <- make_future_dataframe(m, periods = 300, freq = 60 * 60)\n",
"fcst <- predict(m, future)\n", "fcst <- predict(m, future)\n",
"plot(m, fcst)" "plot(m, fcst)"
] ]
}, },
{ {
"cell_type": "code", "cell_type": "code",
"execution_count": 3, "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_yosemite_temps.csv')\n", "df = pd.read_csv('../examples/example_yosemite_temps.csv')\n",
skipping to change at line 136 skipping to change at line 134
}, },
{ {
"cell_type": "markdown", "cell_type": "markdown",
"metadata": {}, "metadata": {},
"source": [ "source": [
"The daily seasonality will show up in the components plot:" "The daily seasonality will show up in the components plot:"
] ]
}, },
{ {
"cell_type": "code", "cell_type": "code",
"execution_count": 5, "execution_count": 4,
"metadata": { "metadata": {
"output_hidden": true "output_hidden": true
}, },
"outputs": [ "outputs": [
{ {
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