"Fossies" - the Fresh Open Source Software Archive  

Source code changes of the file "notebooks/outliers.ipynb" between
prophet-1.1.tar.gz and prophet-1.1.1.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.

outliers.ipynb  (prophet-1.1):outliers.ipynb  (prophet-1.1.1)
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",
"\n",
"from prophet import Prophet\n", "from prophet import Prophet\n",
"import pandas as pd\n", "import pandas as pd\n",
"import logging\n", "import logging\n",
"import warnings\n",
"\n",
"logging.getLogger('prophet').setLevel(logging.ERROR)\n", "logging.getLogger('prophet').setLevel(logging.ERROR)\n",
"logging.getLogger('numexpr').setLevel(logging.ERROR)\n", "logging.getLogger('numexpr').setLevel(logging.ERROR)\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
}, },
"outputs": [ "outputs": [
skipping to change at line 77 skipping to change at line 79
{ {
"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('https://raw.githubusercontent.com/facebook/prophet/main/exa mples/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": 2, "execution_count": 2,
"metadata": {}, "metadata": {},
skipping to change at line 101 skipping to change at line 103
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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('https://raw.githubusercontent.com/facebook/prophet/main/e xamples/example_wp_log_R_outliers1.csv')\n",
"m = Prophet()\n", "m = Prophet()\n",
"m.fit(df)\n", "m.fit(df)\n",
"future = m.make_future_dataframe(periods=1096)\n", "future = m.make_future_dataframe(periods=1096)\n",
"forecast = m.predict(future)\n", "forecast = m.predict(future)\n",
"fig = m.plot(forecast)" "fig = m.plot(forecast)"
] ]
}, },
{ {
"cell_type": "markdown", "cell_type": "markdown",
"metadata": {}, "metadata": {},
skipping to change at line 205 skipping to change at line 207
{ {
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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('https://raw.githubusercontent.com/facebook/prophet/main/exa mples/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": 4, "execution_count": 4,
"metadata": {}, "metadata": {},
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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('https://raw.githubusercontent.com/facebook/prophet/main/e xamples/example_wp_log_R_outliers2.csv')\n",
"m = Prophet()\n", "m = Prophet()\n",
"m.fit(df)\n", "m.fit(df)\n",
"future = m.make_future_dataframe(periods=1096)\n", "future = m.make_future_dataframe(periods=1096)\n",
"forecast = m.predict(future)\n", "forecast = m.predict(future)\n",
"fig = m.plot(forecast)" "fig = m.plot(forecast)"
] ]
}, },
{ {
"cell_type": "markdown", "cell_type": "markdown",
"metadata": {}, "metadata": {},
skipping to change at line 302 skipping to change at line 304
], ],
"source": [ "source": [
"df.loc[(df['ds'] > '2015-06-01') & (df['ds'] < '2015-06-30'), 'y'] = None\n ", "df.loc[(df['ds'] > '2015-06-01') & (df['ds'] < '2015-06-30'), 'y'] = None\n ",
"m = Prophet().fit(df)\n", "m = Prophet().fit(df)\n",
"fig = m.plot(m.predict(future))" "fig = m.plot(m.predict(future))"
] ]
} }
], ],
"metadata": { "metadata": {
"kernelspec": { "kernelspec": {
"display_name": "Python 3", "display_name": "Python 3 (ipykernel)",
"language": "python", "language": "python",
"name": "python3" "name": "python3"
}, },
"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": "3.8.3" "version": "3.8.10"
} }
}, },
"nbformat": 4, "nbformat": 4,
"nbformat_minor": 1 "nbformat_minor": 4
} }
 End of changes. 10 change blocks. 
8 lines changed or deleted 10 lines changed or added

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