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https://github.com/saymrwulf/prophet.git
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Fix artifacts in diagnostics nb markdown
This commit is contained in:
Ben Letham
parent
5e6c6a02f8
commit
6284fc0264
5 changed files with 52 additions and 49 deletions
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@ -40,16 +40,12 @@ head(df.cv)
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# Python
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from fbprophet.diagnostics import cross_validation
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df_cv = cross_validation(m, initial='730 days', period='180 days', horizon = '365 days')
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```
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```python
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# Python
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df_cv.head()
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```
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HBox(children=(FloatProgress(value=0.0, max=11.0), HTML(value='')))
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<div>
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@ -83,8 +79,8 @@ df_cv.head()
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<th>0</th>
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<td>2010-02-16</td>
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<td>8.956828</td>
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<td>8.479812</td>
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<td>9.450908</td>
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<td>8.460272</td>
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<td>9.476844</td>
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<td>8.242493</td>
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<td>2010-02-15</td>
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</tr>
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@ -92,8 +88,8 @@ df_cv.head()
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<th>1</th>
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<td>2010-02-17</td>
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<td>8.723230</td>
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<td>8.213162</td>
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<td>9.217637</td>
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<td>8.208639</td>
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<td>9.222179</td>
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<td>8.008033</td>
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<td>2010-02-15</td>
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</tr>
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@ -101,8 +97,8 @@ df_cv.head()
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<th>2</th>
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<td>2010-02-18</td>
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<td>8.607021</td>
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<td>8.119864</td>
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<td>9.066214</td>
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<td>8.106506</td>
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<td>9.104792</td>
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<td>8.045268</td>
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<td>2010-02-15</td>
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</tr>
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@ -110,8 +106,8 @@ df_cv.head()
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<th>3</th>
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<td>2010-02-19</td>
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<td>8.528870</td>
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<td>8.088676</td>
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<td>9.024842</td>
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<td>8.061701</td>
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<td>9.024450</td>
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<td>7.928766</td>
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<td>2010-02-15</td>
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</tr>
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@ -119,8 +115,8 @@ df_cv.head()
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<th>4</th>
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<td>2010-02-20</td>
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<td>8.270872</td>
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<td>7.740251</td>
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<td>8.760655</td>
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<td>7.773299</td>
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<td>8.745526</td>
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<td>7.745003</td>
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<td>2010-02-15</td>
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</tr>
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@ -147,13 +143,6 @@ df.cv2 <- cross_validation(m, cutoffs = cutoffs, horizon = 365, units = 'days')
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cutoffs = pd.to_datetime(['2013-02-15', '2013-08-15', '2014-02-15'])
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df_cv2 = cross_validation(m, cutoffs=cutoffs, horizon='365 days')
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```
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HBox(children=(FloatProgress(value=0.0, max=3.0), HTML(value='')))
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The `performance_metrics` utility can be used to compute some useful statistics of the prediction performance (`yhat`, `yhat_lower`, and `yhat_upper` compared to `y`), as a function of the distance from the cutoff (how far into the future the prediction was). The statistics computed are mean squared error (MSE), root mean squared error (RMSE), mean absolute error (MAE), mean absolute percent error (MAPE), median absolute percent error (MDAPE) and coverage of the `yhat_lower` and `yhat_upper` estimates. These are computed on a rolling window of the predictions in `df_cv` after sorting by horizon (`ds` minus `cutoff`). By default 10% of the predictions will be included in each window, but this can be changed with the `rolling_window` argument.
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@ -268,7 +257,7 @@ from fbprophet.plot import plot_cross_validation_metric
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fig = plot_cross_validation_metric(df_cv, metric='mape')
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```
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The size of the rolling window in the figure can be changed with the optional argument `rolling_window`, which specifies the proportion of forecasts to use in each rolling window. The default is 0.1, corresponding to 10% of rows from `df_cv` included in each window; increasing this will lead to a smoother average curve in the figure. The `initial` period should be long enough to capture all of the components of the model, in particular seasonalities and extra regressors: at least a year for yearly seasonality, at least a week for weekly seasonality, etc.
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@ -10,7 +10,7 @@
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{
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"data": {
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"application/vnd.jupyter.widget-view+json": {
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"model_id": "622a0104e841488fae81864678a008b3",
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"model_id": "0700e1522a544128889a6f25fa181c2b",
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"version_major": 2,
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"version_minor": 0
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},
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@ -175,13 +175,15 @@
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},
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{
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"cell_type": "code",
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"execution_count": 5,
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"metadata": {},
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"execution_count": 3,
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"metadata": {
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"output_hidden": true
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},
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"outputs": [
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{
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"data": {
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"application/vnd.jupyter.widget-view+json": {
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"model_id": "da408f9d6cf940f3ab28dcc7114e629c",
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"model_id": "d198a659eea54089876a9d87e61a09ff",
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"version_major": 2,
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"version_minor": 0
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},
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@ -198,7 +200,18 @@
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"text": [
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"\n"
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]
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},
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}
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],
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"source": [
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"from fbprophet.diagnostics import cross_validation\n",
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"df_cv = cross_validation(m, initial='730 days', period='180 days', horizon = '365 days')"
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]
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},
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{
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"cell_type": "code",
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"execution_count": 4,
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"metadata": {},
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"outputs": [
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{
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"data": {
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"text/html": [
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@ -233,8 +246,8 @@
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" <th>0</th>\n",
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" <td>2010-02-16</td>\n",
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" <td>8.956828</td>\n",
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" <td>8.479812</td>\n",
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" <td>9.450908</td>\n",
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" <td>8.460272</td>\n",
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" <td>9.476844</td>\n",
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" <td>8.242493</td>\n",
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" <td>2010-02-15</td>\n",
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" </tr>\n",
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@ -242,8 +255,8 @@
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" <th>1</th>\n",
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" <td>2010-02-17</td>\n",
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" <td>8.723230</td>\n",
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" <td>8.213162</td>\n",
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" <td>9.217637</td>\n",
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" <td>8.208639</td>\n",
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" <td>9.222179</td>\n",
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" <td>8.008033</td>\n",
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" <td>2010-02-15</td>\n",
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" </tr>\n",
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@ -251,8 +264,8 @@
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" <th>2</th>\n",
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" <td>2010-02-18</td>\n",
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" <td>8.607021</td>\n",
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" <td>8.119864</td>\n",
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" <td>9.066214</td>\n",
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" <td>8.106506</td>\n",
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" <td>9.104792</td>\n",
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" <td>8.045268</td>\n",
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" <td>2010-02-15</td>\n",
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" </tr>\n",
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@ -260,8 +273,8 @@
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" <th>3</th>\n",
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" <td>2010-02-19</td>\n",
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" <td>8.528870</td>\n",
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" <td>8.088676</td>\n",
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" <td>9.024842</td>\n",
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" <td>8.061701</td>\n",
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" <td>9.024450</td>\n",
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" <td>7.928766</td>\n",
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" <td>2010-02-15</td>\n",
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" </tr>\n",
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@ -269,8 +282,8 @@
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" <th>4</th>\n",
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" <td>2010-02-20</td>\n",
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" <td>8.270872</td>\n",
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" <td>7.740251</td>\n",
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" <td>8.760655</td>\n",
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" <td>7.773299</td>\n",
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" <td>8.745526</td>\n",
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" <td>7.745003</td>\n",
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" <td>2010-02-15</td>\n",
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" </tr>\n",
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@ -280,21 +293,19 @@
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],
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"text/plain": [
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" ds yhat yhat_lower yhat_upper y cutoff\n",
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"0 2010-02-16 8.956828 8.479812 9.450908 8.242493 2010-02-15\n",
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"1 2010-02-17 8.723230 8.213162 9.217637 8.008033 2010-02-15\n",
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"2 2010-02-18 8.607021 8.119864 9.066214 8.045268 2010-02-15\n",
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"3 2010-02-19 8.528870 8.088676 9.024842 7.928766 2010-02-15\n",
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"4 2010-02-20 8.270872 7.740251 8.760655 7.745003 2010-02-15"
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"0 2010-02-16 8.956828 8.460272 9.476844 8.242493 2010-02-15\n",
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"1 2010-02-17 8.723230 8.208639 9.222179 8.008033 2010-02-15\n",
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"2 2010-02-18 8.607021 8.106506 9.104792 8.045268 2010-02-15\n",
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"3 2010-02-19 8.528870 8.061701 9.024450 7.928766 2010-02-15\n",
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"4 2010-02-20 8.270872 7.773299 8.745526 7.745003 2010-02-15"
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]
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},
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"execution_count": 5,
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"execution_count": 4,
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"metadata": {},
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"output_type": "execute_result"
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}
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],
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"source": [
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"from fbprophet.diagnostics import cross_validation\n",
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"df_cv = cross_validation(m, initial='730 days', period='180 days', horizon = '365 days')\n",
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"df_cv.head()"
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]
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},
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@ -321,7 +332,9 @@
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{
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"cell_type": "code",
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"execution_count": 7,
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"metadata": {},
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"metadata": {
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"output_hidden": true
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},
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"outputs": [
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{
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"data": {
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@ -703,6 +716,7 @@
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}
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],
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"metadata": {
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"celltoolbar": "Edit Metadata",
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"kernelspec": {
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"display_name": "Python 3",
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"language": "python",
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