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Tamar Grey committed Feb 17, 2022
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2 changes: 1 addition & 1 deletion docs/source/getting_started/handling_time.ipynb
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},
"source": [
".. note::\n",
" This guide focuses on performing feature engineering on temporal data, but it is not specific to feature engineering for time series problems, which are their own class of machine learning problems. A guide on using Featuretools for time series feature engineering can be found `here <../guides/time_series.ipynb>`_."
" This guide focuses on performing feature engineering on temporal data, but it is not specific to feature engineering for time series problems, which are their own class of machine learning problems. A guide on **using Featuretools for time series feature engineering** can be found `here <../guides/time_series.ipynb>`_."
]
},
{
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6 changes: 3 additions & 3 deletions docs/source/guides/time_series.ipynb
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"metadata": {},
"outputs": [],
"source": [
"datetime_primitives = ['Day', \"Year\", \"Weekday\", \"Month\"]"
"datetime_primitives = [\"Day\", \"Year\", \"Weekday\", \"Month\"]"
]
},
{
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"fm, f = ft.dfs(entityset=es,\n",
" target_dataframe_name='temperatures',\n",
" trans_primitives = (datetime_primitives + \n",
" delaying_primitives + \n",
" delaying_primitives + \n",
" [rolling_mean_primitive, rolling_min_primitive]),\n",
" cutoff_time=pd.Timestamp('1987-1-30')\n",
" )\n",
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"id": "b984ff57",
"metadata": {},
"source": [
"Above is our time series feature matrix! The rolling and delayed features are built from our target column, but do not expose it. We can now use the feature matrix for model building."
"Above is our time series feature matrix! The rolling and delayed features are built from our target column, but do not expose it. We can now use the feature matrix to create a machine learning model that predicts future minimum daily temperatures."
]
}
],
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