"Fossies" - the Fresh Open Source Software Archive  

Source code changes of the file "notebooks/non-daily_data.ipynb" between
prophet-1.0.tar.gz and prophet-1.1.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-1.0):non-daily_data.ipynb  (prophet-1.1)
skipping to change at line 66 skipping to change at line 66
"library(prophet)\n", "library(prophet)\n",
"library(dplyr)" "library(dplyr)"
] ]
}, },
{ {
"cell_type": "markdown", "cell_type": "markdown",
"metadata": {}, "metadata": {},
"source": [ "source": [
"## Sub-daily data\n", "## Sub-daily data\n",
"\n", "\n",
"Prophet can make forecasts for time series with sub-daily observations by p assing in a dataframe with timestamps in the `ds` column. The format of the time stamps should be YYYY-MM-DD HH:MM:SS - see the example csv [here](https://github .com/facebook/prophet/blob/master/examples/example_yosemite_temps.csv). When sub -daily data are used, daily seasonality will automatically be fit. Here we fit P rophet to data with 5-minute resolution (daily temperatures at Yosemite):" "Prophet can make forecasts for time series with sub-daily observations by p assing in a dataframe with timestamps in the `ds` column. The format of the time stamps should be YYYY-MM-DD HH:MM:SS - see the example csv [here](https://github .com/facebook/prophet/blob/main/examples/example_yosemite_temps.csv). When sub-d aily data are used, daily seasonality will automatically be fit. Here we fit Pro phet to data with 5-minute resolution (daily temperatures at Yosemite):"
] ]
}, },
{ {
"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": [
"R[write to console]: Disabling yearly seasonality. Run prophet with yearl y.seasonality=TRUE to override this.\n", "R[write to console]: Disabling yearly seasonality. Run prophet with yearl y.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 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",
skipping to change at line 108 skipping to change at line 108
"plot(m, fcst)" "plot(m, fcst)"
] ]
}, },
{ {
"cell_type": "code", "cell_type": "code",
"execution_count": 2, "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 141 skipping to change at line 141
}, },
{ {
"cell_type": "code", "cell_type": "code",
"execution_count": 4, "execution_count": 4,
"metadata": { "metadata": {
"output_hidden": true "output_hidden": true
}, },
"outputs": [ "outputs": [
{ {
"data": { "data": {
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