| \n", " | date_week | \n", "y | \n", "x1 | \n", "x2 | \n", "event_1 | \n", "event_2 | \n", "dayofyear | \n", "
|---|---|---|---|---|---|---|---|
| 0 | \n", "2018-04-02 | \n", "3.984662 | \n", "0.318580 | \n", "0.0 | \n", "0.0 | \n", "0.0 | \n", "92 | \n", "
| 1 | \n", "2018-04-09 | \n", "3.762872 | \n", "0.112388 | \n", "0.0 | \n", "0.0 | \n", "0.0 | \n", "99 | \n", "
| 2 | \n", "2018-04-16 | \n", "4.466967 | \n", "0.292400 | \n", "0.0 | \n", "0.0 | \n", "0.0 | \n", "106 | \n", "
| 3 | \n", "2018-04-23 | \n", "3.864219 | \n", "0.071399 | \n", "0.0 | \n", "0.0 | \n", "0.0 | \n", "113 | \n", "
| 4 | \n", "2018-04-30 | \n", "4.441625 | \n", "0.386745 | \n", "0.0 | \n", "0.0 | \n", "0.0 | \n", "120 | \n", "
| \n", " | date_week | \n", "y | \n", "x1 | \n", "x2 | \n", "event_1 | \n", "event_2 | \n", "dayofyear | \n", "t | \n", "
|---|---|---|---|---|---|---|---|---|
| 0 | \n", "2018-04-02 | \n", "3.984662 | \n", "0.318580 | \n", "0.0 | \n", "0.0 | \n", "0.0 | \n", "92 | \n", "0 | \n", "
| 1 | \n", "2018-04-09 | \n", "3.762872 | \n", "0.112388 | \n", "0.0 | \n", "0.0 | \n", "0.0 | \n", "99 | \n", "1 | \n", "
| 2 | \n", "2018-04-16 | \n", "4.466967 | \n", "0.292400 | \n", "0.0 | \n", "0.0 | \n", "0.0 | \n", "106 | \n", "2 | \n", "
| 3 | \n", "2018-04-23 | \n", "3.864219 | \n", "0.071399 | \n", "0.0 | \n", "0.0 | \n", "0.0 | \n", "113 | \n", "3 | \n", "
| 4 | \n", "2018-04-30 | \n", "4.441625 | \n", "0.386745 | \n", "0.0 | \n", "0.0 | \n", "0.0 | \n", "120 | \n", "4 | \n", "
<xarray.Dataset> Size: 109MB\n",
"Dimensions: (chain: 4, draw: 1000,\n",
" channel: 2, control: 3,\n",
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"Data variables: (12/18)\n",
" adstock_alpha (chain, draw, channel) float64 64kB ...\n",
" gamma_control (chain, draw, control) float64 96kB ...\n",
" gamma_fourier (chain, draw, fourier_mode) float64 128kB ...\n",
" intercept (chain, draw) float64 32kB ...\n",
" saturation_beta (chain, draw, channel) float64 64kB ...\n",
" saturation_lam (chain, draw, channel) float64 64kB ...\n",
" ... ...\n",
" mu (chain, draw, date) float64 6MB ...\n",
" total_contribution (chain, draw) float64 32kB ...\n",
" total_contribution_original_scale (chain, draw, date) float64 6MB ...\n",
" y_original_scale (chain, draw, date) float64 6MB ...\n",
" yearly_seasonality_contribution (chain, draw, date) float64 6MB ...\n",
" yearly_seasonality_contribution_original_scale (chain, draw, date) float64 6MB ...\n",
"Attributes:\n",
" created_at: 2025-12-03T18:46:08.365760+00:00\n",
" arviz_version: 0.22.0\n",
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" arviz_version: 0.22.0\n",
" inference_library: pymc\n",
" inference_library_version: 5.26.1\n",
" sampling_time: 13.197830200195312\n",
" tuning_steps: 1000<xarray.Dataset> Size: 55MB\n",
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" control_contribution_original_scale (chain, draw, date, control) float64 9MB ...\n",
" fourier_contribution (chain, draw, date, fourier_mode) float64 11MB ...\n",
" ... ...\n",
" total_contribution (chain, draw) float64 16kB ...\n",
" total_contribution_original_scale (chain, draw, date) float64 3MB ...\n",
" y_original_scale (chain, draw, date) float64 3MB ...\n",
" y_sigma (chain, draw) float64 16kB ...\n",
" yearly_seasonality_contribution (chain, draw, date) float64 3MB ...\n",
" yearly_seasonality_contribution_original_scale (chain, draw, date) float64 3MB ...\n",
"Attributes:\n",
" created_at: 2025-12-03T18:45:52.948764+00:00\n",
" arviz_version: 0.22.0\n",
" inference_library: pymc\n",
" inference_library_version: 5.26.1\n",
" pymc_marketing_version: 0.17.0<xarray.Dataset> Size: 3MB\n",
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"Data variables:\n",
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"Attributes:\n",
" created_at: 2025-12-03T18:45:52.953129+00:00\n",
" arviz_version: 0.22.0\n",
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"Attributes:\n",
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" arviz_version: 0.22.0\n",
" inference_library: pymc\n",
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" channel_scale (channel) float64 16B 1.0 1.0\n",
" control_data (date, control) float64 4kB 0.0 0.0 0.0 0.0 ... 0.0 0.0 178.0\n",
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" target_scale float64 8B 8.312\n",
"Attributes:\n",
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" arviz_version: 0.22.0\n",
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"Coordinates:\n",
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"Data variables:\n",
" date_week (index) datetime64[ns] 1kB 2018-04-02 2018-04-09 ... 2021-08-30\n",
" x1 (index) float64 1kB 0.3186 0.1124 0.2924 ... 0.1719 0.2803 0.4389\n",
" x2 (index) float64 1kB 0.0 0.0 0.0 0.0 0.0 ... 0.8633 0.0 0.0 0.0\n",
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" event_2 (index) float64 1kB 0.0 0.0 0.0 0.0 0.0 ... 0.0 0.0 0.0 0.0 0.0\n",
" dayofyear (index) int32 716B 92 99 106 113 120 127 ... 214 221 228 235 242\n",
" t (index) int64 1kB 0 1 2 3 4 5 6 7 ... 172 173 174 175 176 177 178\n",
" y (index) float64 1kB 3.985 3.763 4.467 3.864 ... 4.138 4.479 4.676<xarray.Dataset> Size: 109MB\n",
"Dimensions: (chain: 4, draw: 1000,\n",
" channel: 2, control: 3,\n",
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" * chain (chain) int64 32B 0 1 2 3\n",
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" * fourier_mode (fourier_mode) <U5 80B 's...\n",
" * date (date) datetime64[ns] 1kB ...\n",
"Data variables: (12/18)\n",
" adstock_alpha (chain, draw, channel) float64 64kB ...\n",
" gamma_control (chain, draw, control) float64 96kB ...\n",
" gamma_fourier (chain, draw, fourier_mode) float64 128kB ...\n",
" intercept (chain, draw) float64 32kB ...\n",
" saturation_beta (chain, draw, channel) float64 64kB ...\n",
" saturation_lam (chain, draw, channel) float64 64kB ...\n",
" ... ...\n",
" mu (chain, draw, date) float64 6MB ...\n",
" total_contribution (chain, draw) float64 32kB ...\n",
" total_contribution_original_scale (chain, draw, date) float64 6MB ...\n",
" y_original_scale (chain, draw, date) float64 6MB ...\n",
" yearly_seasonality_contribution (chain, draw, date) float64 6MB ...\n",
" yearly_seasonality_contribution_original_scale (chain, draw, date) float64 6MB ...\n",
"Attributes:\n",
" created_at: 2025-12-03T18:46:08.365760+00:00\n",
" arviz_version: 0.22.0\n",
" inference_library: pymc\n",
" inference_library_version: 5.26.1\n",
" sampling_time: 13.197830200195312\n",
" tuning_steps: 1000\n",
" pymc_marketing_version: 0.17.0| \n", " | mean | \n", "sd | \n", "hdi_3% | \n", "hdi_97% | \n", "mcse_mean | \n", "mcse_sd | \n", "ess_bulk | \n", "ess_tail | \n", "r_hat | \n", "
|---|---|---|---|---|---|---|---|---|---|
| intercept | \n", "0.355 | \n", "0.014 | \n", "0.330 | \n", "0.381 | \n", "0.000 | \n", "0.000 | \n", "2142.0 | \n", "2371.0 | \n", "1.0 | \n", "
| y_sigma | \n", "0.031 | \n", "0.002 | \n", "0.028 | \n", "0.035 | \n", "0.000 | \n", "0.000 | \n", "3324.0 | \n", "2657.0 | \n", "1.0 | \n", "
| saturation_beta[x1] | \n", "0.363 | \n", "0.021 | \n", "0.323 | \n", "0.401 | \n", "0.000 | \n", "0.000 | \n", "1838.0 | \n", "2004.0 | \n", "1.0 | \n", "
| saturation_beta[x2] | \n", "0.267 | \n", "0.079 | \n", "0.192 | \n", "0.388 | \n", "0.002 | \n", "0.006 | \n", "1440.0 | \n", "1087.0 | \n", "1.0 | \n", "
| saturation_lam[x1] | \n", "3.948 | \n", "0.396 | \n", "3.226 | \n", "4.701 | \n", "0.008 | \n", "0.007 | \n", "2347.0 | \n", "2021.0 | \n", "1.0 | \n", "
| saturation_lam[x2] | \n", "3.201 | \n", "1.221 | \n", "0.981 | \n", "5.453 | \n", "0.032 | \n", "0.032 | \n", "1397.0 | \n", "1118.0 | \n", "1.0 | \n", "
| adstock_alpha[x1] | \n", "0.402 | \n", "0.032 | \n", "0.345 | \n", "0.464 | \n", "0.001 | \n", "0.001 | \n", "2245.0 | \n", "2295.0 | \n", "1.0 | \n", "
| adstock_alpha[x2] | \n", "0.186 | \n", "0.041 | \n", "0.108 | \n", "0.264 | \n", "0.001 | \n", "0.001 | \n", "1946.0 | \n", "2031.0 | \n", "1.0 | \n", "
| gamma_control[event_1] | \n", "0.175 | \n", "0.027 | \n", "0.122 | \n", "0.225 | \n", "0.000 | \n", "0.000 | \n", "3546.0 | \n", "2695.0 | \n", "1.0 | \n", "
| gamma_control[event_2] | \n", "0.231 | \n", "0.028 | \n", "0.180 | \n", "0.283 | \n", "0.000 | \n", "0.000 | \n", "3738.0 | \n", "2772.0 | \n", "1.0 | \n", "
| gamma_control[t] | \n", "0.001 | \n", "0.000 | \n", "0.001 | \n", "0.001 | \n", "0.000 | \n", "0.000 | \n", "2664.0 | \n", "2426.0 | \n", "1.0 | \n", "
| gamma_fourier[sin_1] | \n", "0.003 | \n", "0.004 | \n", "-0.004 | \n", "0.010 | \n", "0.000 | \n", "0.000 | \n", "3700.0 | \n", "2904.0 | \n", "1.0 | \n", "
| gamma_fourier[sin_2] | \n", "-0.058 | \n", "0.003 | \n", "-0.065 | \n", "-0.051 | \n", "0.000 | \n", "0.000 | \n", "3976.0 | \n", "3247.0 | \n", "1.0 | \n", "
| gamma_fourier[cos_1] | \n", "0.062 | \n", "0.003 | \n", "0.057 | \n", "0.069 | \n", "0.000 | \n", "0.000 | \n", "4170.0 | \n", "3065.0 | \n", "1.0 | \n", "
| gamma_fourier[cos_2] | \n", "0.001 | \n", "0.004 | \n", "-0.006 | \n", "0.007 | \n", "0.000 | \n", "0.000 | \n", "2832.0 | \n", "2928.0 | \n", "1.0 | \n", "
<xarray.Dataset> Size: 6MB\n",
"Dimensions: (date: 179, sample: 4000)\n",
"Coordinates:\n",
" * date (date) datetime64[ns] 1kB 2018-04-02 2018-04-09 ... 2021-08-30\n",
" * sample (sample) object 32kB MultiIndex\n",
" * chain (sample) int64 32kB 0 0 0 0 0 0 0 0 0 0 0 ... 3 3 3 3 3 3 3 3 3 3 3\n",
" * draw (sample) int64 32kB 0 1 2 3 4 5 6 7 ... 993 994 995 996 997 998 999\n",
"Data variables:\n",
" y (date, sample) float64 6MB 4.322 4.116 4.01 ... 4.691 4.686 4.638\n",
"Attributes:\n",
" created_at: 2025-12-03T18:46:10.829012+00:00\n",
" arviz_version: 0.22.0\n",
" inference_library: pymc\n",
" inference_library_version: 5.26.1Pipeline(steps=[('scaler', MaxAbsScaler())])In a Jupyter environment, please rerun this cell to show the HTML representation or trust the notebook. | \n", " | steps | \n", "[('scaler', ...)] | \n", "
| \n", " | transform_input | \n", "None | \n", "
| \n", " | memory | \n", "None | \n", "
| \n", " | verbose | \n", "False | \n", "
| \n", " | copy | \n", "True | \n", "
| \n", " | x1 | \n", "x2 | \n", "event_1 | \n", "event_2 | \n", "t | \n", "yearly_seasonality | \n", "intercept | \n", "
|---|---|---|---|---|---|---|---|
| date | \n", "\n", " | \n", " | \n", " | \n", " | \n", " | \n", " | \n", " |
| 2018-04-02 | \n", "1.078665 | \n", "0.0 | \n", "0.0 | \n", "0.0 | \n", "0.000000 | \n", "0.021584 | \n", "2.950783 | \n", "
| 2018-04-09 | \n", "0.829730 | \n", "0.0 | \n", "0.0 | \n", "0.0 | \n", "0.005138 | \n", "0.073686 | \n", "2.950783 | \n", "
| 2018-04-16 | \n", "1.289344 | \n", "0.0 | \n", "0.0 | \n", "0.0 | \n", "0.010276 | \n", "0.119578 | \n", "2.950783 | \n", "
| 2018-04-23 | \n", "0.789177 | \n", "0.0 | \n", "0.0 | \n", "0.0 | \n", "0.015414 | \n", "0.153941 | \n", "2.950783 | \n", "
| 2018-04-30 | \n", "1.535418 | \n", "0.0 | \n", "0.0 | \n", "0.0 | \n", "0.020552 | \n", "0.172189 | \n", "2.950783 | \n", "
| \n", " | date_week | \n", "x1 | \n", "x2 | \n", "event_1 | \n", "event_2 | \n", "t | \n", "
|---|---|---|---|---|---|---|
| 0 | \n", "2021-09-06 | \n", "0.438857 | \n", "0.0 | \n", "0 | \n", "0 | \n", "179 | \n", "
| 1 | \n", "2021-09-13 | \n", "0.438857 | \n", "0.0 | \n", "0 | \n", "0 | \n", "180 | \n", "
| 2 | \n", "2021-09-20 | \n", "0.438857 | \n", "0.0 | \n", "0 | \n", "0 | \n", "181 | \n", "
| 3 | \n", "2021-09-27 | \n", "0.438857 | \n", "0.0 | \n", "0 | \n", "0 | \n", "182 | \n", "
| 4 | \n", "2021-10-04 | \n", "0.438857 | \n", "0.0 | \n", "0 | \n", "0 | \n", "183 | \n", "
<xarray.Dataset> Size: 256kB\n",
"Dimensions: (date: 5, sample: 4000)\n",
"Coordinates:\n",
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" * draw (sample) int64 32kB 0 1 2 3 4 5 6 7 ... 993 994 995 996 997 998 999\n",
"Data variables:\n",
" y (date, sample) float64 160kB 4.994 4.472 4.861 ... 6.35 6.16 5.687\n",
"Attributes:\n",
" created_at: 2025-12-03T18:46:21.156088+00:00\n",
" arviz_version: 0.22.0\n",
" inference_library: pymc\n",
" inference_library_version: 5.26.1