\n", " | Description | \n", "Value | \n", "
---|---|---|
0 | \n", "session_id | \n", "123 | \n", "
1 | \n", "Target | \n", "Number of airline passengers | \n", "
2 | \n", "Approach | \n", "Univariate | \n", "
3 | \n", "Exogenous Variables | \n", "Not Present | \n", "
4 | \n", "Original data shape | \n", "(144, 1) | \n", "
5 | \n", "Transformed data shape | \n", "(144, 1) | \n", "
6 | \n", "Transformed train set shape | \n", "(141, 1) | \n", "
7 | \n", "Transformed test set shape | \n", "(3, 1) | \n", "
8 | \n", "Rows with missing values | \n", "0.0% | \n", "
9 | \n", "Fold Generator | \n", "ExpandingWindowSplitter | \n", "
10 | \n", "Fold Number | \n", "3 | \n", "
11 | \n", "Enforce Prediction Interval | \n", "False | \n", "
12 | \n", "Splits used for hyperparameters | \n", "all | \n", "
13 | \n", "Seasonality Detection Algo | \n", "auto | \n", "
14 | \n", "Max Period to Consider | \n", "60 | \n", "
15 | \n", "Seasonal Period(s) Tested | \n", "[12, 24, 36, 11, 48] | \n", "
16 | \n", "Significant Seasonal Period(s) | \n", "[12, 24, 36, 11, 48] | \n", "
17 | \n", "Significant Seasonal Period(s) without Harmonics | \n", "[48, 36, 11] | \n", "
18 | \n", "Remove Harmonics | \n", "False | \n", "
19 | \n", "Harmonics Order Method | \n", "harmonic_max | \n", "
20 | \n", "Num Seasonalities to Use | \n", "1 | \n", "
21 | \n", "All Seasonalities to Use | \n", "[12] | \n", "
22 | \n", "Primary Seasonality | \n", "12 | \n", "
23 | \n", "Seasonality Present | \n", "True | \n", "
24 | \n", "Target Strictly Positive | \n", "True | \n", "
25 | \n", "Target White Noise | \n", "No | \n", "
26 | \n", "Recommended d | \n", "1 | \n", "
27 | \n", "Recommended Seasonal D | \n", "1 | \n", "
28 | \n", "Preprocess | \n", "False | \n", "
29 | \n", "CPU Jobs | \n", "-1 | \n", "
30 | \n", "Use GPU | \n", "False | \n", "
31 | \n", "Log Experiment | \n", "False | \n", "
32 | \n", "Experiment Name | \n", "ts-default-name | \n", "
33 | \n", "USI | \n", "4a01 | \n", "
\n", " | Description | \n", "Value | \n", "
---|---|---|
0 | \n", "session_id | \n", "123 | \n", "
1 | \n", "Target | \n", "Number of airline passengers | \n", "
2 | \n", "Approach | \n", "Univariate | \n", "
3 | \n", "Exogenous Variables | \n", "Not Present | \n", "
4 | \n", "Original data shape | \n", "(144, 1) | \n", "
5 | \n", "Transformed data shape | \n", "(144, 1) | \n", "
6 | \n", "Transformed train set shape | \n", "(141, 1) | \n", "
7 | \n", "Transformed test set shape | \n", "(3, 1) | \n", "
8 | \n", "Rows with missing values | \n", "0.0% | \n", "
9 | \n", "Fold Generator | \n", "ExpandingWindowSplitter | \n", "
10 | \n", "Fold Number | \n", "3 | \n", "
11 | \n", "Enforce Prediction Interval | \n", "False | \n", "
12 | \n", "Splits used for hyperparameters | \n", "all | \n", "
13 | \n", "Seasonality Detection Algo | \n", "auto | \n", "
14 | \n", "Max Period to Consider | \n", "60 | \n", "
15 | \n", "Seasonal Period(s) Tested | \n", "[12, 24, 36, 11, 48] | \n", "
16 | \n", "Significant Seasonal Period(s) | \n", "[12, 24, 36, 11, 48] | \n", "
17 | \n", "Significant Seasonal Period(s) without Harmonics | \n", "[48, 36, 11] | \n", "
18 | \n", "Remove Harmonics | \n", "False | \n", "
19 | \n", "Harmonics Order Method | \n", "harmonic_max | \n", "
20 | \n", "Num Seasonalities to Use | \n", "1 | \n", "
21 | \n", "All Seasonalities to Use | \n", "[12] | \n", "
22 | \n", "Primary Seasonality | \n", "12 | \n", "
23 | \n", "Seasonality Present | \n", "True | \n", "
24 | \n", "Target Strictly Positive | \n", "True | \n", "
25 | \n", "Target White Noise | \n", "No | \n", "
26 | \n", "Recommended d | \n", "1 | \n", "
27 | \n", "Recommended Seasonal D | \n", "1 | \n", "
28 | \n", "Preprocess | \n", "False | \n", "
29 | \n", "CPU Jobs | \n", "-1 | \n", "
30 | \n", "Use GPU | \n", "False | \n", "
31 | \n", "Log Experiment | \n", "False | \n", "
32 | \n", "Experiment Name | \n", "ts-default-name | \n", "
33 | \n", "USI | \n", "cf71 | \n", "
\n", " | Test | \n", "Test Name | \n", "Data | \n", "Property | \n", "Setting | \n", "Value | \n", "
---|---|---|---|---|---|---|
0 | \n", "Summary | \n", "Statistics | \n", "Transformed | \n", "Length | \n", "\n", " | 144.0 | \n", "
1 | \n", "Summary | \n", "Statistics | \n", "Transformed | \n", "# Missing Values | \n", "\n", " | 0.0 | \n", "
2 | \n", "Summary | \n", "Statistics | \n", "Transformed | \n", "Mean | \n", "\n", " | 280.298611 | \n", "
3 | \n", "Summary | \n", "Statistics | \n", "Transformed | \n", "Median | \n", "\n", " | 265.5 | \n", "
4 | \n", "Summary | \n", "Statistics | \n", "Transformed | \n", "Standard Deviation | \n", "\n", " | 119.966317 | \n", "
5 | \n", "Summary | \n", "Statistics | \n", "Transformed | \n", "Variance | \n", "\n", " | 14391.917201 | \n", "
6 | \n", "Summary | \n", "Statistics | \n", "Transformed | \n", "Kurtosis | \n", "\n", " | -0.364942 | \n", "
7 | \n", "Summary | \n", "Statistics | \n", "Transformed | \n", "Skewness | \n", "\n", " | 0.58316 | \n", "
8 | \n", "Summary | \n", "Statistics | \n", "Transformed | \n", "# Distinct Values | \n", "\n", " | 118.0 | \n", "
9 | \n", "White Noise | \n", "Ljung-Box | \n", "Transformed | \n", "Test Statictic | \n", "{'alpha': 0.05, 'K': 24} | \n", "1606.083817 | \n", "
10 | \n", "White Noise | \n", "Ljung-Box | \n", "Transformed | \n", "Test Statictic | \n", "{'alpha': 0.05, 'K': 48} | \n", "1933.155822 | \n", "
11 | \n", "White Noise | \n", "Ljung-Box | \n", "Transformed | \n", "p-value | \n", "{'alpha': 0.05, 'K': 24} | \n", "0.0 | \n", "
12 | \n", "White Noise | \n", "Ljung-Box | \n", "Transformed | \n", "p-value | \n", "{'alpha': 0.05, 'K': 48} | \n", "0.0 | \n", "
13 | \n", "White Noise | \n", "Ljung-Box | \n", "Transformed | \n", "White Noise | \n", "{'alpha': 0.05, 'K': 24} | \n", "False | \n", "
14 | \n", "White Noise | \n", "Ljung-Box | \n", "Transformed | \n", "White Noise | \n", "{'alpha': 0.05, 'K': 48} | \n", "False | \n", "
15 | \n", "Stationarity | \n", "ADF | \n", "Transformed | \n", "Stationarity | \n", "{'alpha': 0.05} | \n", "False | \n", "
16 | \n", "Stationarity | \n", "ADF | \n", "Transformed | \n", "p-value | \n", "{'alpha': 0.05} | \n", "0.99188 | \n", "
17 | \n", "Stationarity | \n", "ADF | \n", "Transformed | \n", "Test Statistic | \n", "{'alpha': 0.05} | \n", "0.815369 | \n", "
18 | \n", "Stationarity | \n", "ADF | \n", "Transformed | \n", "Critical Value 1% | \n", "{'alpha': 0.05} | \n", "-3.481682 | \n", "
19 | \n", "Stationarity | \n", "ADF | \n", "Transformed | \n", "Critical Value 5% | \n", "{'alpha': 0.05} | \n", "-2.884042 | \n", "
20 | \n", "Stationarity | \n", "ADF | \n", "Transformed | \n", "Critical Value 10% | \n", "{'alpha': 0.05} | \n", "-2.57877 | \n", "
21 | \n", "Stationarity | \n", "KPSS | \n", "Transformed | \n", "Trend Stationarity | \n", "{'alpha': 0.05} | \n", "True | \n", "
22 | \n", "Stationarity | \n", "KPSS | \n", "Transformed | \n", "p-value | \n", "{'alpha': 0.05} | \n", "0.1 | \n", "
23 | \n", "Stationarity | \n", "KPSS | \n", "Transformed | \n", "Test Statistic | \n", "{'alpha': 0.05} | \n", "0.09615 | \n", "
24 | \n", "Stationarity | \n", "KPSS | \n", "Transformed | \n", "Critical Value 10% | \n", "{'alpha': 0.05} | \n", "0.119 | \n", "
25 | \n", "Stationarity | \n", "KPSS | \n", "Transformed | \n", "Critical Value 5% | \n", "{'alpha': 0.05} | \n", "0.146 | \n", "
26 | \n", "Stationarity | \n", "KPSS | \n", "Transformed | \n", "Critical Value 2.5% | \n", "{'alpha': 0.05} | \n", "0.176 | \n", "
27 | \n", "Stationarity | \n", "KPSS | \n", "Transformed | \n", "Critical Value 1% | \n", "{'alpha': 0.05} | \n", "0.216 | \n", "
28 | \n", "Normality | \n", "Shapiro | \n", "Transformed | \n", "Normality | \n", "{'alpha': 0.05} | \n", "False | \n", "
29 | \n", "Normality | \n", "Shapiro | \n", "Transformed | \n", "p-value | \n", "{'alpha': 0.05} | \n", "0.000068 | \n", "
\n", " | Model | \n", "MASE | \n", "RMSSE | \n", "MAE | \n", "RMSE | \n", "MAPE | \n", "SMAPE | \n", "R2 | \n", "TT (Sec) | \n", "
---|---|---|---|---|---|---|---|---|---|
ets | \n", "ETS | \n", "0.4912 | \n", "0.5541 | \n", "15.0940 | \n", "19.3099 | \n", "0.0318 | \n", "0.0316 | \n", "-0.4465 | \n", "0.0967 | \n", "
exp_smooth | \n", "Exponential Smoothing | \n", "0.4929 | \n", "0.5560 | \n", "15.1460 | \n", "19.3779 | \n", "0.0320 | \n", "0.0317 | \n", "-0.4600 | \n", "0.1033 | \n", "
arima | \n", "ARIMA | \n", "0.6964 | \n", "0.7110 | \n", "21.3757 | \n", "24.7774 | \n", "0.0447 | \n", "0.0456 | \n", "-0.5495 | \n", "0.0667 | \n", "
auto_arima | \n", "Auto ARIMA | \n", "0.7136 | \n", "0.6945 | \n", "21.9389 | \n", "24.2138 | \n", "0.0459 | \n", "0.0464 | \n", "-0.5454 | \n", "9.6867 | \n", "
par_cds_dt | \n", "Passive Aggressive w/ Cond. Deseasonalize & Detrending | \n", "0.7212 | \n", "0.6696 | \n", "22.1794 | \n", "23.3673 | \n", "0.0453 | \n", "0.0468 | \n", "0.0261 | \n", "0.1200 | \n", "
lar_cds_dt | \n", "Least Angular Regressor w/ Cond. Deseasonalize & Detrending | \n", "0.8503 | \n", "0.8261 | \n", "26.2655 | \n", "28.9830 | \n", "0.0513 | \n", "0.0534 | \n", "0.0367 | \n", "0.0967 | \n", "
huber_cds_dt | \n", "Huber w/ Cond. Deseasonalize & Detrending | \n", "0.8658 | \n", "0.8362 | \n", "26.7826 | \n", "29.3947 | \n", "0.0516 | \n", "0.0536 | \n", "0.1501 | \n", "0.1333 | \n", "
lr_cds_dt | \n", "Linear w/ Cond. Deseasonalize & Detrending | \n", "0.8904 | \n", "0.8722 | \n", "27.5266 | \n", "30.6243 | \n", "0.0534 | \n", "0.0555 | \n", "-0.0092 | \n", "0.4067 | \n", "
ridge_cds_dt | \n", "Ridge w/ Cond. Deseasonalize & Detrending | \n", "0.8905 | \n", "0.8722 | \n", "27.5270 | \n", "30.6246 | \n", "0.0534 | \n", "0.0555 | \n", "-0.0092 | \n", "0.2933 | \n", "
en_cds_dt | \n", "Elastic Net w/ Cond. Deseasonalize & Detrending | \n", "0.8944 | \n", "0.8746 | \n", "27.6535 | \n", "30.7127 | \n", "0.0535 | \n", "0.0557 | \n", "-0.0063 | \n", "0.3833 | \n", "
lasso_cds_dt | \n", "Lasso w/ Cond. Deseasonalize & Detrending | \n", "0.8966 | \n", "0.8759 | \n", "27.7231 | \n", "30.7594 | \n", "0.0536 | \n", "0.0558 | \n", "-0.0040 | \n", "0.1033 | \n", "
br_cds_dt | \n", "Bayesian Ridge w/ Cond. Deseasonalize & Detrending | \n", "0.9156 | \n", "0.8878 | \n", "28.3188 | \n", "31.1821 | \n", "0.0547 | \n", "0.0569 | \n", "-0.0209 | \n", "0.1067 | \n", "
knn_cds_dt | \n", "K Neighbors w/ Cond. Deseasonalize & Detrending | \n", "1.0695 | \n", "0.9924 | \n", "33.1500 | \n", "34.9277 | \n", "0.0631 | \n", "0.0656 | \n", "-0.1682 | \n", "0.1233 | \n", "
theta | \n", "Theta Forecaster | \n", "1.0839 | \n", "1.0393 | \n", "33.3223 | \n", "36.2555 | \n", "0.0686 | \n", "0.0710 | \n", "-1.7926 | \n", "0.0333 | \n", "
et_cds_dt | \n", "Extra Trees w/ Cond. Deseasonalize & Detrending | \n", "1.1678 | \n", "1.0866 | \n", "36.1678 | \n", "38.2100 | \n", "0.0694 | \n", "0.0726 | \n", "-0.4302 | \n", "0.1900 | \n", "
dt_cds_dt | \n", "Decision Tree w/ Cond. Deseasonalize & Detrending | \n", "1.1930 | \n", "1.1346 | \n", "36.9106 | \n", "39.8518 | \n", "0.0733 | \n", "0.0769 | \n", "-0.8135 | \n", "0.1300 | \n", "
lightgbm_cds_dt | \n", "Light Gradient Boosting w/ Cond. Deseasonalize & Detrending | \n", "1.2019 | \n", "1.1362 | \n", "37.2359 | \n", "39.9827 | \n", "0.0713 | \n", "0.0746 | \n", "-0.6051 | \n", "0.6633 | \n", "
omp_cds_dt | \n", "Orthogonal Matching Pursuit w/ Cond. Deseasonalize & Detrending | \n", "1.2171 | \n", "1.1475 | \n", "37.6457 | \n", "40.3070 | \n", "0.0724 | \n", "0.0757 | \n", "-0.7057 | \n", "0.1067 | \n", "
gbr_cds_dt | \n", "Gradient Boosting w/ Cond. Deseasonalize & Detrending | \n", "1.2274 | \n", "1.1449 | \n", "37.9963 | \n", "40.2550 | \n", "0.0735 | \n", "0.0769 | \n", "-0.7190 | \n", "0.1467 | \n", "
rf_cds_dt | \n", "Random Forest w/ Cond. Deseasonalize & Detrending | \n", "1.2500 | \n", "1.1782 | \n", "38.6418 | \n", "41.3528 | \n", "0.0749 | \n", "0.0784 | \n", "-0.9426 | \n", "0.2133 | \n", "
catboost_cds_dt | \n", "CatBoost Regressor w/ Cond. Deseasonalize & Detrending | \n", "1.2523 | \n", "1.1604 | \n", "38.8002 | \n", "40.8201 | \n", "0.0745 | \n", "0.0780 | \n", "-0.6842 | \n", "1.5933 | \n", "
ada_cds_dt | \n", "AdaBoost w/ Cond. Deseasonalize & Detrending | \n", "1.2786 | \n", "1.1951 | \n", "39.6382 | \n", "42.0658 | \n", "0.0750 | \n", "0.0788 | \n", "-0.6308 | \n", "0.1367 | \n", "
xgboost_cds_dt | \n", "Extreme Gradient Boosting w/ Cond. Deseasonalize & Detrending | \n", "1.3198 | \n", "1.2045 | \n", "40.8342 | \n", "42.3045 | \n", "0.0792 | \n", "0.0831 | \n", "-0.9192 | \n", "0.1800 | \n", "
llar_cds_dt | \n", "Lasso Least Angular Regressor w/ Cond. Deseasonalize & Detrending | \n", "1.3659 | \n", "1.2672 | \n", "42.3974 | \n", "44.6597 | \n", "0.0793 | \n", "0.0834 | \n", "-0.7393 | \n", "0.0967 | \n", "
naive | \n", "Naive Forecaster | \n", "1.5654 | \n", "1.4951 | \n", "48.4444 | \n", "52.5232 | \n", "0.0920 | \n", "0.0981 | \n", "-1.8344 | \n", "2.5533 | \n", "
snaive | \n", "Seasonal Naive Forecaster | \n", "1.6741 | \n", "1.5343 | \n", "51.6667 | \n", "53.7350 | \n", "0.1052 | \n", "0.1117 | \n", "-4.5388 | \n", "1.2567 | \n", "
polytrend | \n", "Polynomial Trend Forecaster | \n", "2.1553 | \n", "2.1096 | \n", "66.9817 | \n", "74.4048 | \n", "0.1241 | \n", "0.1350 | \n", "-4.2525 | \n", "0.0167 | \n", "
croston | \n", "Croston | \n", "2.4565 | \n", "2.3513 | \n", "76.3953 | \n", "82.9794 | \n", "0.1394 | \n", "0.1562 | \n", "-4.5895 | \n", "0.0167 | \n", "
grand_means | \n", "Grand Means Forecaster | \n", "7.3065 | \n", "6.5029 | \n", "226.0502 | \n", "228.3880 | \n", "0.4469 | \n", "0.5821 | \n", "-72.1183 | \n", "1.4433 | \n", "
\n", " | Model | \n", "MASE | \n", "RMSSE | \n", "MAE | \n", "RMSE | \n", "MAPE | \n", "SMAPE | \n", "R2 | \n", "TT (Sec) | \n", "
---|---|---|---|---|---|---|---|---|---|
ets | \n", "ETS | \n", "0.4912 | \n", "0.5541 | \n", "15.0940 | \n", "19.3099 | \n", "0.0318 | \n", "0.0316 | \n", "-0.4465 | \n", "0.0967 | \n", "
exp_smooth | \n", "Exponential Smoothing | \n", "0.4929 | \n", "0.5560 | \n", "15.1460 | \n", "19.3779 | \n", "0.0320 | \n", "0.0317 | \n", "-0.4600 | \n", "0.0867 | \n", "
arima | \n", "ARIMA | \n", "0.6964 | \n", "0.7110 | \n", "21.3757 | \n", "24.7774 | \n", "0.0447 | \n", "0.0456 | \n", "-0.5495 | \n", "0.1300 | \n", "
auto_arima | \n", "Auto ARIMA | \n", "0.7136 | \n", "0.6945 | \n", "21.9389 | \n", "24.2138 | \n", "0.0459 | \n", "0.0464 | \n", "-0.5454 | \n", "13.9433 | \n", "
par_cds_dt | \n", "Passive Aggressive w/ Cond. Deseasonalize & Detrending | \n", "0.7212 | \n", "0.6696 | \n", "22.1794 | \n", "23.3673 | \n", "0.0453 | \n", "0.0468 | \n", "0.0261 | \n", "0.1100 | \n", "
lar_cds_dt | \n", "Least Angular Regressor w/ Cond. Deseasonalize & Detrending | \n", "0.8503 | \n", "0.8261 | \n", "26.2655 | \n", "28.9830 | \n", "0.0513 | \n", "0.0534 | \n", "0.0367 | \n", "0.1200 | \n", "
huber_cds_dt | \n", "Huber w/ Cond. Deseasonalize & Detrending | \n", "0.8658 | \n", "0.8362 | \n", "26.7826 | \n", "29.3947 | \n", "0.0516 | \n", "0.0536 | \n", "0.1501 | \n", "0.0967 | \n", "
lr_cds_dt | \n", "Linear w/ Cond. Deseasonalize & Detrending | \n", "0.8904 | \n", "0.8722 | \n", "27.5266 | \n", "30.6243 | \n", "0.0534 | \n", "0.0555 | \n", "-0.0092 | \n", "0.0967 | \n", "
ridge_cds_dt | \n", "Ridge w/ Cond. Deseasonalize & Detrending | \n", "0.8905 | \n", "0.8722 | \n", "27.5270 | \n", "30.6246 | \n", "0.0534 | \n", "0.0555 | \n", "-0.0092 | \n", "0.0967 | \n", "
en_cds_dt | \n", "Elastic Net w/ Cond. Deseasonalize & Detrending | \n", "0.8944 | \n", "0.8746 | \n", "27.6535 | \n", "30.7127 | \n", "0.0535 | \n", "0.0557 | \n", "-0.0063 | \n", "0.1133 | \n", "
lasso_cds_dt | \n", "Lasso w/ Cond. Deseasonalize & Detrending | \n", "0.8966 | \n", "0.8759 | \n", "27.7231 | \n", "30.7594 | \n", "0.0536 | \n", "0.0558 | \n", "-0.0040 | \n", "0.0933 | \n", "
br_cds_dt | \n", "Bayesian Ridge w/ Cond. Deseasonalize & Detrending | \n", "0.9156 | \n", "0.8878 | \n", "28.3188 | \n", "31.1821 | \n", "0.0547 | \n", "0.0569 | \n", "-0.0209 | \n", "0.0900 | \n", "
knn_cds_dt | \n", "K Neighbors w/ Cond. Deseasonalize & Detrending | \n", "1.0695 | \n", "0.9924 | \n", "33.1500 | \n", "34.9277 | \n", "0.0631 | \n", "0.0656 | \n", "-0.1682 | \n", "0.1300 | \n", "
theta | \n", "Theta Forecaster | \n", "1.0839 | \n", "1.0393 | \n", "33.3223 | \n", "36.2555 | \n", "0.0686 | \n", "0.0710 | \n", "-1.7926 | \n", "0.0300 | \n", "
et_cds_dt | \n", "Extra Trees w/ Cond. Deseasonalize & Detrending | \n", "1.1678 | \n", "1.0866 | \n", "36.1678 | \n", "38.2100 | \n", "0.0694 | \n", "0.0726 | \n", "-0.4302 | \n", "0.2600 | \n", "
dt_cds_dt | \n", "Decision Tree w/ Cond. Deseasonalize & Detrending | \n", "1.1930 | \n", "1.1346 | \n", "36.9106 | \n", "39.8518 | \n", "0.0733 | \n", "0.0769 | \n", "-0.8135 | \n", "0.1767 | \n", "
lightgbm_cds_dt | \n", "Light Gradient Boosting w/ Cond. Deseasonalize & Detrending | \n", "1.2019 | \n", "1.1362 | \n", "37.2359 | \n", "39.9827 | \n", "0.0713 | \n", "0.0746 | \n", "-0.6051 | \n", "0.5800 | \n", "
omp_cds_dt | \n", "Orthogonal Matching Pursuit w/ Cond. Deseasonalize & Detrending | \n", "1.2171 | \n", "1.1475 | \n", "37.6457 | \n", "40.3070 | \n", "0.0724 | \n", "0.0757 | \n", "-0.7057 | \n", "0.1167 | \n", "
gbr_cds_dt | \n", "Gradient Boosting w/ Cond. Deseasonalize & Detrending | \n", "1.2274 | \n", "1.1449 | \n", "37.9963 | \n", "40.2550 | \n", "0.0735 | \n", "0.0769 | \n", "-0.7190 | \n", "0.1633 | \n", "
rf_cds_dt | \n", "Random Forest w/ Cond. Deseasonalize & Detrending | \n", "1.2500 | \n", "1.1782 | \n", "38.6418 | \n", "41.3528 | \n", "0.0749 | \n", "0.0784 | \n", "-0.9426 | \n", "0.2433 | \n", "
catboost_cds_dt | \n", "CatBoost Regressor w/ Cond. Deseasonalize & Detrending | \n", "1.2523 | \n", "1.1604 | \n", "38.8002 | \n", "40.8201 | \n", "0.0745 | \n", "0.0780 | \n", "-0.6842 | \n", "1.6900 | \n", "
ada_cds_dt | \n", "AdaBoost w/ Cond. Deseasonalize & Detrending | \n", "1.2786 | \n", "1.1951 | \n", "39.6382 | \n", "42.0658 | \n", "0.0750 | \n", "0.0788 | \n", "-0.6308 | \n", "0.1733 | \n", "
xgboost_cds_dt | \n", "Extreme Gradient Boosting w/ Cond. Deseasonalize & Detrending | \n", "1.3198 | \n", "1.2045 | \n", "40.8342 | \n", "42.3045 | \n", "0.0792 | \n", "0.0831 | \n", "-0.9192 | \n", "0.2167 | \n", "
llar_cds_dt | \n", "Lasso Least Angular Regressor w/ Cond. Deseasonalize & Detrending | \n", "1.3659 | \n", "1.2672 | \n", "42.3974 | \n", "44.6597 | \n", "0.0793 | \n", "0.0834 | \n", "-0.7393 | \n", "0.0967 | \n", "
naive | \n", "Naive Forecaster | \n", "1.5654 | \n", "1.4951 | \n", "48.4444 | \n", "52.5232 | \n", "0.0920 | \n", "0.0981 | \n", "-1.8344 | \n", "0.0467 | \n", "
snaive | \n", "Seasonal Naive Forecaster | \n", "1.6741 | \n", "1.5343 | \n", "51.6667 | \n", "53.7350 | \n", "0.1052 | \n", "0.1117 | \n", "-4.5388 | \n", "0.0367 | \n", "
polytrend | \n", "Polynomial Trend Forecaster | \n", "2.1553 | \n", "2.1096 | \n", "66.9817 | \n", "74.4048 | \n", "0.1241 | \n", "0.1350 | \n", "-4.2525 | \n", "0.0433 | \n", "
croston | \n", "Croston | \n", "2.4565 | \n", "2.3513 | \n", "76.3953 | \n", "82.9794 | \n", "0.1394 | \n", "0.1562 | \n", "-4.5895 | \n", "0.0267 | \n", "
grand_means | \n", "Grand Means Forecaster | \n", "7.3065 | \n", "6.5029 | \n", "226.0502 | \n", "228.3880 | \n", "0.4469 | \n", "0.5821 | \n", "-72.1183 | \n", "0.0400 | \n", "
AutoETS(seasonal='mul', sp=12, trend='add')In a Jupyter environment, please rerun this cell to show the HTML representation or trust the notebook.
AutoETS(seasonal='mul', sp=12, trend='add')
\n", " | Model | \n", "MASE | \n", "RMSSE | \n", "MAE | \n", "RMSE | \n", "MAPE | \n", "SMAPE | \n", "R2 | \n", "
---|---|---|---|---|---|---|---|---|
0 | \n", "ETS | \n", "0.2516 | \n", "0.2962 | \n", "8.0352 | \n", "10.7426 | \n", "0.0179 | \n", "0.0182 | \n", "0.8642 | \n", "
\n", " | y_pred | \n", "
---|---|
1960-10 | \n", "442.9857 | \n", "
1960-11 | \n", "388.2084 | \n", "
1960-12 | \n", "427.7002 | \n", "
\n", " | y_pred | \n", "
---|---|
1960-10 | \n", "442.9857 | \n", "
1960-11 | \n", "388.2084 | \n", "
1960-12 | \n", "427.7002 | \n", "
1961-01 | \n", "440.8284 | \n", "
1961-02 | \n", "414.1669 | \n", "
1961-03 | \n", "460.3102 | \n", "
1961-04 | \n", "489.8039 | \n", "
1961-05 | \n", "500.6157 | \n", "
1961-06 | \n", "567.9574 | \n", "
1961-07 | \n", "657.4232 | \n", "
1961-08 | \n", "648.7133 | \n", "
1961-09 | \n", "541.5302 | \n", "
1961-10 | \n", "472.1907 | \n", "
1961-11 | \n", "413.6623 | \n", "
1961-12 | \n", "455.5911 | \n", "
1962-01 | \n", "469.4199 | \n", "
1962-02 | \n", "440.8848 | \n", "
1962-03 | \n", "489.8460 | \n", "
1962-04 | \n", "521.0650 | \n", "
1962-05 | \n", "532.3979 | \n", "
1962-06 | \n", "603.8251 | \n", "
1962-07 | \n", "698.7234 | \n", "
1962-08 | \n", "689.2542 | \n", "
1962-09 | \n", "575.1974 | \n", "
1962-10 | \n", "501.3958 | \n", "
1962-11 | \n", "439.1161 | \n", "
1962-12 | \n", "483.4819 | \n", "
1963-01 | \n", "498.0115 | \n", "
1963-02 | \n", "467.6027 | \n", "
1963-03 | \n", "519.3819 | \n", "
1963-04 | \n", "552.3262 | \n", "
1963-05 | \n", "564.1800 | \n", "
1963-06 | \n", "639.6927 | \n", "
1963-07 | \n", "740.0237 | \n", "
1963-08 | \n", "729.7950 | \n", "
1963-09 | \n", "608.8646 | \n", "
AutoETS(seasonal='mul', sp=12, trend='add')In a Jupyter environment, please rerun this cell to show the HTML representation or trust the notebook.
AutoETS(seasonal='mul', sp=12, trend='add')
\n", " | Description | \n", "Value | \n", "
---|---|---|
0 | \n", "session_id | \n", "123 | \n", "
1 | \n", "Target | \n", "Number of airline passengers | \n", "
2 | \n", "Approach | \n", "Univariate | \n", "
3 | \n", "Exogenous Variables | \n", "Not Present | \n", "
4 | \n", "Original data shape | \n", "(144, 1) | \n", "
5 | \n", "Transformed data shape | \n", "(144, 1) | \n", "
6 | \n", "Transformed train set shape | \n", "(141, 1) | \n", "
7 | \n", "Transformed test set shape | \n", "(3, 1) | \n", "
8 | \n", "Rows with missing values | \n", "0.0% | \n", "
9 | \n", "Fold Generator | \n", "ExpandingWindowSplitter | \n", "
10 | \n", "Fold Number | \n", "3 | \n", "
11 | \n", "Enforce Prediction Interval | \n", "False | \n", "
12 | \n", "Splits used for hyperparameters | \n", "all | \n", "
13 | \n", "Seasonality Detection Algo | \n", "auto | \n", "
14 | \n", "Max Period to Consider | \n", "60 | \n", "
15 | \n", "Seasonal Period(s) Tested | \n", "[12, 24, 36, 11, 48] | \n", "
16 | \n", "Significant Seasonal Period(s) | \n", "[12, 24, 36, 11, 48] | \n", "
17 | \n", "Significant Seasonal Period(s) without Harmonics | \n", "[48, 36, 11] | \n", "
18 | \n", "Remove Harmonics | \n", "False | \n", "
19 | \n", "Harmonics Order Method | \n", "harmonic_max | \n", "
20 | \n", "Num Seasonalities to Use | \n", "1 | \n", "
21 | \n", "All Seasonalities to Use | \n", "[12] | \n", "
22 | \n", "Primary Seasonality | \n", "12 | \n", "
23 | \n", "Seasonality Present | \n", "True | \n", "
24 | \n", "Target Strictly Positive | \n", "True | \n", "
25 | \n", "Target White Noise | \n", "No | \n", "
26 | \n", "Recommended d | \n", "1 | \n", "
27 | \n", "Recommended Seasonal D | \n", "1 | \n", "
28 | \n", "Preprocess | \n", "False | \n", "
29 | \n", "CPU Jobs | \n", "-1 | \n", "
30 | \n", "Use GPU | \n", "False | \n", "
31 | \n", "Log Experiment | \n", "False | \n", "
32 | \n", "Experiment Name | \n", "ts-default-name | \n", "
33 | \n", "USI | \n", "0889 | \n", "
\n", " | Description | \n", "Value | \n", "
---|---|---|
0 | \n", "session_id | \n", "123 | \n", "
1 | \n", "Target | \n", "Number of airline passengers | \n", "
2 | \n", "Approach | \n", "Univariate | \n", "
3 | \n", "Exogenous Variables | \n", "Not Present | \n", "
4 | \n", "Original data shape | \n", "(144, 1) | \n", "
5 | \n", "Transformed data shape | \n", "(144, 1) | \n", "
6 | \n", "Transformed train set shape | \n", "(141, 1) | \n", "
7 | \n", "Transformed test set shape | \n", "(3, 1) | \n", "
8 | \n", "Rows with missing values | \n", "0.0% | \n", "
9 | \n", "Fold Generator | \n", "ExpandingWindowSplitter | \n", "
10 | \n", "Fold Number | \n", "3 | \n", "
11 | \n", "Enforce Prediction Interval | \n", "False | \n", "
12 | \n", "Splits used for hyperparameters | \n", "all | \n", "
13 | \n", "Seasonality Detection Algo | \n", "auto | \n", "
14 | \n", "Max Period to Consider | \n", "60 | \n", "
15 | \n", "Seasonal Period(s) Tested | \n", "[12, 24, 36, 11, 48] | \n", "
16 | \n", "Significant Seasonal Period(s) | \n", "[12, 24, 36, 11, 48] | \n", "
17 | \n", "Significant Seasonal Period(s) without Harmonics | \n", "[48, 36, 11] | \n", "
18 | \n", "Remove Harmonics | \n", "False | \n", "
19 | \n", "Harmonics Order Method | \n", "harmonic_max | \n", "
20 | \n", "Num Seasonalities to Use | \n", "1 | \n", "
21 | \n", "All Seasonalities to Use | \n", "[12] | \n", "
22 | \n", "Primary Seasonality | \n", "12 | \n", "
23 | \n", "Seasonality Present | \n", "True | \n", "
24 | \n", "Target Strictly Positive | \n", "True | \n", "
25 | \n", "Target White Noise | \n", "No | \n", "
26 | \n", "Recommended d | \n", "1 | \n", "
27 | \n", "Recommended Seasonal D | \n", "1 | \n", "
28 | \n", "Preprocess | \n", "True | \n", "
29 | \n", "Numerical Imputation (Target) | \n", "drift | \n", "
30 | \n", "Transformation (Target) | \n", "None | \n", "
31 | \n", "Scaling (Target) | \n", "None | \n", "
32 | \n", "Feature Engineering (Target) - Reduced Regression | \n", "False | \n", "
33 | \n", "CPU Jobs | \n", "-1 | \n", "
34 | \n", "Use GPU | \n", "False | \n", "
35 | \n", "Log Experiment | \n", "False | \n", "
36 | \n", "Experiment Name | \n", "ts-default-name | \n", "
37 | \n", "USI | \n", "b1f7 | \n", "
\n", " | Model | \n", "MASE | \n", "RMSSE | \n", "MAE | \n", "RMSE | \n", "MAPE | \n", "SMAPE | \n", "R2 | \n", "TT (Sec) | \n", "
---|---|---|---|---|---|---|---|---|---|
ets | \n", "ETS | \n", "0.4912 | \n", "0.5541 | \n", "15.0940 | \n", "19.3099 | \n", "0.0318 | \n", "0.0316 | \n", "-0.4465 | \n", "0.1100 | \n", "
exp_smooth | \n", "Exponential Smoothing | \n", "0.4929 | \n", "0.5560 | \n", "15.1460 | \n", "19.3779 | \n", "0.0320 | \n", "0.0317 | \n", "-0.4600 | \n", "0.1067 | \n", "
arima | \n", "ARIMA | \n", "0.6964 | \n", "0.7110 | \n", "21.3757 | \n", "24.7774 | \n", "0.0447 | \n", "0.0456 | \n", "-0.5495 | \n", "0.1167 | \n", "
auto_arima | \n", "Auto ARIMA | \n", "0.7136 | \n", "0.6945 | \n", "21.9389 | \n", "24.2138 | \n", "0.0459 | \n", "0.0464 | \n", "-0.5454 | \n", "11.6333 | \n", "
par_cds_dt | \n", "Passive Aggressive w/ Cond. Deseasonalize & Detrending | \n", "0.7212 | \n", "0.6696 | \n", "22.1794 | \n", "23.3673 | \n", "0.0453 | \n", "0.0468 | \n", "0.0261 | \n", "0.1267 | \n", "
lar_cds_dt | \n", "Least Angular Regressor w/ Cond. Deseasonalize & Detrending | \n", "0.8503 | \n", "0.8261 | \n", "26.2655 | \n", "28.9830 | \n", "0.0513 | \n", "0.0534 | \n", "0.0367 | \n", "0.1167 | \n", "
huber_cds_dt | \n", "Huber w/ Cond. Deseasonalize & Detrending | \n", "0.8658 | \n", "0.8362 | \n", "26.7826 | \n", "29.3947 | \n", "0.0516 | \n", "0.0536 | \n", "0.1501 | \n", "0.1267 | \n", "
lr_cds_dt | \n", "Linear w/ Cond. Deseasonalize & Detrending | \n", "0.8904 | \n", "0.8722 | \n", "27.5266 | \n", "30.6243 | \n", "0.0534 | \n", "0.0555 | \n", "-0.0092 | \n", "0.1300 | \n", "
ridge_cds_dt | \n", "Ridge w/ Cond. Deseasonalize & Detrending | \n", "0.8905 | \n", "0.8722 | \n", "27.5270 | \n", "30.6246 | \n", "0.0534 | \n", "0.0555 | \n", "-0.0092 | \n", "0.1333 | \n", "
en_cds_dt | \n", "Elastic Net w/ Cond. Deseasonalize & Detrending | \n", "0.8944 | \n", "0.8746 | \n", "27.6535 | \n", "30.7127 | \n", "0.0535 | \n", "0.0557 | \n", "-0.0063 | \n", "0.1333 | \n", "
lasso_cds_dt | \n", "Lasso w/ Cond. Deseasonalize & Detrending | \n", "0.8966 | \n", "0.8759 | \n", "27.7231 | \n", "30.7594 | \n", "0.0536 | \n", "0.0558 | \n", "-0.0040 | \n", "0.1233 | \n", "
br_cds_dt | \n", "Bayesian Ridge w/ Cond. Deseasonalize & Detrending | \n", "0.9156 | \n", "0.8878 | \n", "28.3188 | \n", "31.1821 | \n", "0.0547 | \n", "0.0569 | \n", "-0.0209 | \n", "0.1167 | \n", "
knn_cds_dt | \n", "K Neighbors w/ Cond. Deseasonalize & Detrending | \n", "1.0695 | \n", "0.9924 | \n", "33.1500 | \n", "34.9277 | \n", "0.0631 | \n", "0.0656 | \n", "-0.1682 | \n", "0.1433 | \n", "
theta | \n", "Theta Forecaster | \n", "1.0839 | \n", "1.0393 | \n", "33.3223 | \n", "36.2555 | \n", "0.0686 | \n", "0.0710 | \n", "-1.7926 | \n", "0.0600 | \n", "
et_cds_dt | \n", "Extra Trees w/ Cond. Deseasonalize & Detrending | \n", "1.1678 | \n", "1.0866 | \n", "36.1678 | \n", "38.2100 | \n", "0.0694 | \n", "0.0726 | \n", "-0.4302 | \n", "0.2033 | \n", "
dt_cds_dt | \n", "Decision Tree w/ Cond. Deseasonalize & Detrending | \n", "1.1930 | \n", "1.1346 | \n", "36.9106 | \n", "39.8518 | \n", "0.0733 | \n", "0.0769 | \n", "-0.8135 | \n", "0.1233 | \n", "
lightgbm_cds_dt | \n", "Light Gradient Boosting w/ Cond. Deseasonalize & Detrending | \n", "1.2019 | \n", "1.1362 | \n", "37.2359 | \n", "39.9827 | \n", "0.0713 | \n", "0.0746 | \n", "-0.6051 | \n", "0.3767 | \n", "
omp_cds_dt | \n", "Orthogonal Matching Pursuit w/ Cond. Deseasonalize & Detrending | \n", "1.2171 | \n", "1.1475 | \n", "37.6457 | \n", "40.3070 | \n", "0.0724 | \n", "0.0757 | \n", "-0.7057 | \n", "0.1200 | \n", "
gbr_cds_dt | \n", "Gradient Boosting w/ Cond. Deseasonalize & Detrending | \n", "1.2274 | \n", "1.1449 | \n", "37.9963 | \n", "40.2550 | \n", "0.0735 | \n", "0.0769 | \n", "-0.7190 | \n", "0.1567 | \n", "
rf_cds_dt | \n", "Random Forest w/ Cond. Deseasonalize & Detrending | \n", "1.2500 | \n", "1.1782 | \n", "38.6418 | \n", "41.3528 | \n", "0.0749 | \n", "0.0784 | \n", "-0.9426 | \n", "0.2267 | \n", "
catboost_cds_dt | \n", "CatBoost Regressor w/ Cond. Deseasonalize & Detrending | \n", "1.2523 | \n", "1.1604 | \n", "38.8002 | \n", "40.8201 | \n", "0.0745 | \n", "0.0780 | \n", "-0.6842 | \n", "1.0700 | \n", "
ada_cds_dt | \n", "AdaBoost w/ Cond. Deseasonalize & Detrending | \n", "1.2786 | \n", "1.1951 | \n", "39.6382 | \n", "42.0658 | \n", "0.0750 | \n", "0.0788 | \n", "-0.6308 | \n", "0.1433 | \n", "
xgboost_cds_dt | \n", "Extreme Gradient Boosting w/ Cond. Deseasonalize & Detrending | \n", "1.3198 | \n", "1.2045 | \n", "40.8342 | \n", "42.3045 | \n", "0.0792 | \n", "0.0831 | \n", "-0.9192 | \n", "0.1400 | \n", "
llar_cds_dt | \n", "Lasso Least Angular Regressor w/ Cond. Deseasonalize & Detrending | \n", "1.3659 | \n", "1.2672 | \n", "42.3974 | \n", "44.6597 | \n", "0.0793 | \n", "0.0834 | \n", "-0.7393 | \n", "0.1167 | \n", "
naive | \n", "Naive Forecaster | \n", "1.5654 | \n", "1.4951 | \n", "48.4444 | \n", "52.5232 | \n", "0.0920 | \n", "0.0981 | \n", "-1.8344 | \n", "0.1067 | \n", "
snaive | \n", "Seasonal Naive Forecaster | \n", "1.6741 | \n", "1.5343 | \n", "51.6667 | \n", "53.7350 | \n", "0.1052 | \n", "0.1117 | \n", "-4.5388 | \n", "0.0733 | \n", "
polytrend | \n", "Polynomial Trend Forecaster | \n", "2.1553 | \n", "2.1096 | \n", "66.9817 | \n", "74.4048 | \n", "0.1241 | \n", "0.1350 | \n", "-4.2525 | \n", "0.0567 | \n", "
croston | \n", "Croston | \n", "2.4565 | \n", "2.3513 | \n", "76.3953 | \n", "82.9794 | \n", "0.1394 | \n", "0.1562 | \n", "-4.5895 | \n", "0.0433 | \n", "
grand_means | \n", "Grand Means Forecaster | \n", "7.3065 | \n", "6.5029 | \n", "226.0502 | \n", "228.3880 | \n", "0.4469 | \n", "0.5821 | \n", "-72.1183 | \n", "0.0733 | \n", "
\n", " | Name | \n", "Reference | \n", "Turbo | \n", "
---|---|---|---|
ID | \n", "\n", " | \n", " | \n", " |
naive | \n", "Naive Forecaster | \n", "sktime.forecasting.naive.NaiveForecaster | \n", "True | \n", "
grand_means | \n", "Grand Means Forecaster | \n", "sktime.forecasting.naive.NaiveForecaster | \n", "True | \n", "
snaive | \n", "Seasonal Naive Forecaster | \n", "sktime.forecasting.naive.NaiveForecaster | \n", "True | \n", "
polytrend | \n", "Polynomial Trend Forecaster | \n", "sktime.forecasting.trend.PolynomialTrendForeca... | \n", "True | \n", "
arima | \n", "ARIMA | \n", "sktime.forecasting.arima.ARIMA | \n", "True | \n", "
auto_arima | \n", "Auto ARIMA | \n", "sktime.forecasting.arima.AutoARIMA | \n", "True | \n", "
exp_smooth | \n", "Exponential Smoothing | \n", "sktime.forecasting.exp_smoothing.ExponentialSm... | \n", "True | \n", "
croston | \n", "Croston | \n", "sktime.forecasting.croston.Croston | \n", "True | \n", "
ets | \n", "ETS | \n", "sktime.forecasting.ets.AutoETS | \n", "True | \n", "
theta | \n", "Theta Forecaster | \n", "sktime.forecasting.theta.ThetaForecaster | \n", "True | \n", "
tbats | \n", "TBATS | \n", "sktime.forecasting.tbats.TBATS | \n", "False | \n", "
bats | \n", "BATS | \n", "sktime.forecasting.bats.BATS | \n", "False | \n", "
lr_cds_dt | \n", "Linear w/ Cond. Deseasonalize & Detrending | \n", "pycaret.containers.models.time_series.BaseCdsD... | \n", "True | \n", "
en_cds_dt | \n", "Elastic Net w/ Cond. Deseasonalize & Detrending | \n", "pycaret.containers.models.time_series.BaseCdsD... | \n", "True | \n", "
ridge_cds_dt | \n", "Ridge w/ Cond. Deseasonalize & Detrending | \n", "pycaret.containers.models.time_series.BaseCdsD... | \n", "True | \n", "
lasso_cds_dt | \n", "Lasso w/ Cond. Deseasonalize & Detrending | \n", "pycaret.containers.models.time_series.BaseCdsD... | \n", "True | \n", "
lar_cds_dt | \n", "Least Angular Regressor w/ Cond. Deseasonalize... | \n", "pycaret.containers.models.time_series.BaseCdsD... | \n", "True | \n", "
llar_cds_dt | \n", "Lasso Least Angular Regressor w/ Cond. Deseaso... | \n", "pycaret.containers.models.time_series.BaseCdsD... | \n", "True | \n", "
br_cds_dt | \n", "Bayesian Ridge w/ Cond. Deseasonalize & Detren... | \n", "pycaret.containers.models.time_series.BaseCdsD... | \n", "True | \n", "
huber_cds_dt | \n", "Huber w/ Cond. Deseasonalize & Detrending | \n", "pycaret.containers.models.time_series.BaseCdsD... | \n", "True | \n", "
par_cds_dt | \n", "Passive Aggressive w/ Cond. Deseasonalize & De... | \n", "pycaret.containers.models.time_series.BaseCdsD... | \n", "True | \n", "
omp_cds_dt | \n", "Orthogonal Matching Pursuit w/ Cond. Deseasona... | \n", "pycaret.containers.models.time_series.BaseCdsD... | \n", "True | \n", "
knn_cds_dt | \n", "K Neighbors w/ Cond. Deseasonalize & Detrending | \n", "pycaret.containers.models.time_series.BaseCdsD... | \n", "True | \n", "
dt_cds_dt | \n", "Decision Tree w/ Cond. Deseasonalize & Detrending | \n", "pycaret.containers.models.time_series.BaseCdsD... | \n", "True | \n", "
rf_cds_dt | \n", "Random Forest w/ Cond. Deseasonalize & Detrending | \n", "pycaret.containers.models.time_series.BaseCdsD... | \n", "True | \n", "
et_cds_dt | \n", "Extra Trees w/ Cond. Deseasonalize & Detrending | \n", "pycaret.containers.models.time_series.BaseCdsD... | \n", "True | \n", "
gbr_cds_dt | \n", "Gradient Boosting w/ Cond. Deseasonalize & Det... | \n", "pycaret.containers.models.time_series.BaseCdsD... | \n", "True | \n", "
ada_cds_dt | \n", "AdaBoost w/ Cond. Deseasonalize & Detrending | \n", "pycaret.containers.models.time_series.BaseCdsD... | \n", "True | \n", "
xgboost_cds_dt | \n", "Extreme Gradient Boosting w/ Cond. Deseasonali... | \n", "pycaret.containers.models.time_series.BaseCdsD... | \n", "True | \n", "
lightgbm_cds_dt | \n", "Light Gradient Boosting w/ Cond. Deseasonalize... | \n", "pycaret.containers.models.time_series.BaseCdsD... | \n", "True | \n", "
catboost_cds_dt | \n", "CatBoost Regressor w/ Cond. Deseasonalize & De... | \n", "pycaret.containers.models.time_series.BaseCdsD... | \n", "True | \n", "
\n", " | Model | \n", "MASE | \n", "RMSSE | \n", "MAE | \n", "RMSE | \n", "MAPE | \n", "SMAPE | \n", "R2 | \n", "TT (Sec) | \n", "
---|---|---|---|---|---|---|---|---|---|
ets | \n", "ETS | \n", "0.4912 | \n", "0.5541 | \n", "15.0940 | \n", "19.3099 | \n", "0.0318 | \n", "0.0316 | \n", "-0.4465 | \n", "0.1033 | \n", "
arima | \n", "ARIMA | \n", "0.6964 | \n", "0.7110 | \n", "21.3757 | \n", "24.7774 | \n", "0.0447 | \n", "0.0456 | \n", "-0.5495 | \n", "0.0800 | \n", "
theta | \n", "Theta Forecaster | \n", "1.0839 | \n", "1.0393 | \n", "33.3223 | \n", "36.2555 | \n", "0.0686 | \n", "0.0710 | \n", "-1.7926 | \n", "0.0500 | \n", "
naive | \n", "Naive Forecaster | \n", "1.5654 | \n", "1.4951 | \n", "48.4444 | \n", "52.5232 | \n", "0.0920 | \n", "0.0981 | \n", "-1.8344 | \n", "0.0467 | \n", "
snaive | \n", "Seasonal Naive Forecaster | \n", "1.6741 | \n", "1.5343 | \n", "51.6667 | \n", "53.7350 | \n", "0.1052 | \n", "0.1117 | \n", "-4.5388 | \n", "0.0400 | \n", "
polytrend | \n", "Polynomial Trend Forecaster | \n", "2.1553 | \n", "2.1096 | \n", "66.9817 | \n", "74.4048 | \n", "0.1241 | \n", "0.1350 | \n", "-4.2525 | \n", "0.0500 | \n", "
grand_means | \n", "Grand Means Forecaster | \n", "7.3065 | \n", "6.5029 | \n", "226.0502 | \n", "228.3880 | \n", "0.4469 | \n", "0.5821 | \n", "-72.1183 | \n", "0.0500 | \n", "
AutoETS(seasonal='mul', sp=12, trend='add')In a Jupyter environment, please rerun this cell to show the HTML representation or trust the notebook.
AutoETS(seasonal='mul', sp=12, trend='add')
\n", " | Model | \n", "MASE | \n", "RMSSE | \n", "MAE | \n", "RMSE | \n", "MAPE | \n", "SMAPE | \n", "R2 | \n", "TT (Sec) | \n", "
---|---|---|---|---|---|---|---|---|---|
ets | \n", "ETS | \n", "0.4912 | \n", "0.5541 | \n", "15.094 | \n", "19.3099 | \n", "0.0318 | \n", "0.0316 | \n", "-0.4465 | \n", "0.1033 | \n", "
arima | \n", "ARIMA | \n", "0.6964 | \n", "0.711 | \n", "21.3757 | \n", "24.7774 | \n", "0.0447 | \n", "0.0456 | \n", "-0.5495 | \n", "0.0800 | \n", "
theta | \n", "Theta Forecaster | \n", "1.0839 | \n", "1.0393 | \n", "33.3223 | \n", "36.2555 | \n", "0.0686 | \n", "0.071 | \n", "-1.7926 | \n", "0.0500 | \n", "
naive | \n", "Naive Forecaster | \n", "1.5654 | \n", "1.4951 | \n", "48.4444 | \n", "52.5232 | \n", "0.092 | \n", "0.0981 | \n", "-1.8344 | \n", "0.0467 | \n", "
snaive | \n", "Seasonal Naive Forecaster | \n", "1.6741 | \n", "1.5343 | \n", "51.6667 | \n", "53.735 | \n", "0.1052 | \n", "0.1117 | \n", "-4.5388 | \n", "0.0400 | \n", "
polytrend | \n", "Polynomial Trend Forecaster | \n", "2.1553 | \n", "2.1096 | \n", "66.9817 | \n", "74.4048 | \n", "0.1241 | \n", "0.135 | \n", "-4.2525 | \n", "0.0500 | \n", "
grand_means | \n", "Grand Means Forecaster | \n", "7.3065 | \n", "6.5029 | \n", "226.0502 | \n", "228.388 | \n", "0.4469 | \n", "0.5821 | \n", "-72.1183 | \n", "0.0500 | \n", "
\n", " | Model | \n", "MASE | \n", "RMSSE | \n", "MAE | \n", "RMSE | \n", "MAPE | \n", "SMAPE | \n", "R2 | \n", "TT (Sec) | \n", "
---|---|---|---|---|---|---|---|---|---|
huber_cds_dt | \n", "Huber w/ Cond. Deseasonalize & Detrending | \n", "0.8658 | \n", "0.8362 | \n", "26.7826 | \n", "29.3947 | \n", "0.0516 | \n", "0.0536 | \n", "0.1501 | \n", "0.1300 | \n", "
lar_cds_dt | \n", "Least Angular Regressor w/ Cond. Deseasonalize & Detrending | \n", "0.8503 | \n", "0.8261 | \n", "26.2655 | \n", "28.9830 | \n", "0.0513 | \n", "0.0534 | \n", "0.0367 | \n", "0.1067 | \n", "
par_cds_dt | \n", "Passive Aggressive w/ Cond. Deseasonalize & Detrending | \n", "0.7212 | \n", "0.6696 | \n", "22.1794 | \n", "23.3673 | \n", "0.0453 | \n", "0.0468 | \n", "0.0261 | \n", "0.1100 | \n", "
lasso_cds_dt | \n", "Lasso w/ Cond. Deseasonalize & Detrending | \n", "0.8966 | \n", "0.8759 | \n", "27.7231 | \n", "30.7594 | \n", "0.0536 | \n", "0.0558 | \n", "-0.0040 | \n", "0.1133 | \n", "
en_cds_dt | \n", "Elastic Net w/ Cond. Deseasonalize & Detrending | \n", "0.8944 | \n", "0.8746 | \n", "27.6535 | \n", "30.7127 | \n", "0.0535 | \n", "0.0557 | \n", "-0.0063 | \n", "0.1133 | \n", "
lr_cds_dt | \n", "Linear w/ Cond. Deseasonalize & Detrending | \n", "0.8904 | \n", "0.8722 | \n", "27.5266 | \n", "30.6243 | \n", "0.0534 | \n", "0.0555 | \n", "-0.0092 | \n", "0.1200 | \n", "
ridge_cds_dt | \n", "Ridge w/ Cond. Deseasonalize & Detrending | \n", "0.8905 | \n", "0.8722 | \n", "27.5270 | \n", "30.6246 | \n", "0.0534 | \n", "0.0555 | \n", "-0.0092 | \n", "0.1167 | \n", "
br_cds_dt | \n", "Bayesian Ridge w/ Cond. Deseasonalize & Detrending | \n", "0.9156 | \n", "0.8878 | \n", "28.3188 | \n", "31.1821 | \n", "0.0547 | \n", "0.0569 | \n", "-0.0209 | \n", "0.1267 | \n", "
knn_cds_dt | \n", "K Neighbors w/ Cond. Deseasonalize & Detrending | \n", "1.0695 | \n", "0.9924 | \n", "33.1500 | \n", "34.9277 | \n", "0.0631 | \n", "0.0656 | \n", "-0.1682 | \n", "0.1367 | \n", "
et_cds_dt | \n", "Extra Trees w/ Cond. Deseasonalize & Detrending | \n", "1.1678 | \n", "1.0866 | \n", "36.1678 | \n", "38.2100 | \n", "0.0694 | \n", "0.0726 | \n", "-0.4302 | \n", "0.2133 | \n", "
ets | \n", "ETS | \n", "0.4912 | \n", "0.5541 | \n", "15.0940 | \n", "19.3099 | \n", "0.0318 | \n", "0.0316 | \n", "-0.4465 | \n", "0.1000 | \n", "
exp_smooth | \n", "Exponential Smoothing | \n", "0.4929 | \n", "0.5560 | \n", "15.1460 | \n", "19.3779 | \n", "0.0320 | \n", "0.0317 | \n", "-0.4600 | \n", "0.1033 | \n", "
auto_arima | \n", "Auto ARIMA | \n", "0.7136 | \n", "0.6945 | \n", "21.9389 | \n", "24.2138 | \n", "0.0459 | \n", "0.0464 | \n", "-0.5454 | \n", "11.7400 | \n", "
arima | \n", "ARIMA | \n", "0.6964 | \n", "0.7110 | \n", "21.3757 | \n", "24.7774 | \n", "0.0447 | \n", "0.0456 | \n", "-0.5495 | \n", "0.0867 | \n", "
lightgbm_cds_dt | \n", "Light Gradient Boosting w/ Cond. Deseasonalize & Detrending | \n", "1.2019 | \n", "1.1362 | \n", "37.2359 | \n", "39.9827 | \n", "0.0713 | \n", "0.0746 | \n", "-0.6051 | \n", "0.3667 | \n", "
ada_cds_dt | \n", "AdaBoost w/ Cond. Deseasonalize & Detrending | \n", "1.2786 | \n", "1.1951 | \n", "39.6382 | \n", "42.0658 | \n", "0.0750 | \n", "0.0788 | \n", "-0.6308 | \n", "0.1433 | \n", "
catboost_cds_dt | \n", "CatBoost Regressor w/ Cond. Deseasonalize & Detrending | \n", "1.2523 | \n", "1.1604 | \n", "38.8002 | \n", "40.8201 | \n", "0.0745 | \n", "0.0780 | \n", "-0.6842 | \n", "1.1400 | \n", "
omp_cds_dt | \n", "Orthogonal Matching Pursuit w/ Cond. Deseasonalize & Detrending | \n", "1.2171 | \n", "1.1475 | \n", "37.6457 | \n", "40.3070 | \n", "0.0724 | \n", "0.0757 | \n", "-0.7057 | \n", "0.1200 | \n", "
gbr_cds_dt | \n", "Gradient Boosting w/ Cond. Deseasonalize & Detrending | \n", "1.2274 | \n", "1.1449 | \n", "37.9963 | \n", "40.2550 | \n", "0.0735 | \n", "0.0769 | \n", "-0.7190 | \n", "0.1333 | \n", "
llar_cds_dt | \n", "Lasso Least Angular Regressor w/ Cond. Deseasonalize & Detrending | \n", "1.3659 | \n", "1.2672 | \n", "42.3974 | \n", "44.6597 | \n", "0.0793 | \n", "0.0834 | \n", "-0.7393 | \n", "0.1033 | \n", "
dt_cds_dt | \n", "Decision Tree w/ Cond. Deseasonalize & Detrending | \n", "1.1930 | \n", "1.1346 | \n", "36.9106 | \n", "39.8518 | \n", "0.0733 | \n", "0.0769 | \n", "-0.8135 | \n", "0.1200 | \n", "
xgboost_cds_dt | \n", "Extreme Gradient Boosting w/ Cond. Deseasonalize & Detrending | \n", "1.3198 | \n", "1.2045 | \n", "40.8342 | \n", "42.3045 | \n", "0.0792 | \n", "0.0831 | \n", "-0.9192 | \n", "0.1333 | \n", "
rf_cds_dt | \n", "Random Forest w/ Cond. Deseasonalize & Detrending | \n", "1.2500 | \n", "1.1782 | \n", "38.6418 | \n", "41.3528 | \n", "0.0749 | \n", "0.0784 | \n", "-0.9426 | \n", "0.2033 | \n", "
theta | \n", "Theta Forecaster | \n", "1.0839 | \n", "1.0393 | \n", "33.3223 | \n", "36.2555 | \n", "0.0686 | \n", "0.0710 | \n", "-1.7926 | \n", "0.0600 | \n", "
naive | \n", "Naive Forecaster | \n", "1.5654 | \n", "1.4951 | \n", "48.4444 | \n", "52.5232 | \n", "0.0920 | \n", "0.0981 | \n", "-1.8344 | \n", "0.0500 | \n", "
polytrend | \n", "Polynomial Trend Forecaster | \n", "2.1553 | \n", "2.1096 | \n", "66.9817 | \n", "74.4048 | \n", "0.1241 | \n", "0.1350 | \n", "-4.2525 | \n", "0.0500 | \n", "
snaive | \n", "Seasonal Naive Forecaster | \n", "1.6741 | \n", "1.5343 | \n", "51.6667 | \n", "53.7350 | \n", "0.1052 | \n", "0.1117 | \n", "-4.5388 | \n", "0.0600 | \n", "
croston | \n", "Croston | \n", "2.4565 | \n", "2.3513 | \n", "76.3953 | \n", "82.9794 | \n", "0.1394 | \n", "0.1562 | \n", "-4.5895 | \n", "0.0433 | \n", "
grand_means | \n", "Grand Means Forecaster | \n", "7.3065 | \n", "6.5029 | \n", "226.0502 | \n", "228.3880 | \n", "0.4469 | \n", "0.5821 | \n", "-72.1183 | \n", "0.0633 | \n", "
\n", " | Test | \n", "Test Name | \n", "Data | \n", "Property | \n", "Setting | \n", "Value | \n", "
---|---|---|---|---|---|---|
0 | \n", "Summary | \n", "Statistics | \n", "Transformed | \n", "Length | \n", "\n", " | 144.0 | \n", "
1 | \n", "Summary | \n", "Statistics | \n", "Transformed | \n", "# Missing Values | \n", "\n", " | 0.0 | \n", "
2 | \n", "Summary | \n", "Statistics | \n", "Transformed | \n", "Mean | \n", "\n", " | 280.298611 | \n", "
3 | \n", "Summary | \n", "Statistics | \n", "Transformed | \n", "Median | \n", "\n", " | 265.5 | \n", "
4 | \n", "Summary | \n", "Statistics | \n", "Transformed | \n", "Standard Deviation | \n", "\n", " | 119.966317 | \n", "
5 | \n", "Summary | \n", "Statistics | \n", "Transformed | \n", "Variance | \n", "\n", " | 14391.917201 | \n", "
6 | \n", "Summary | \n", "Statistics | \n", "Transformed | \n", "Kurtosis | \n", "\n", " | -0.364942 | \n", "
7 | \n", "Summary | \n", "Statistics | \n", "Transformed | \n", "Skewness | \n", "\n", " | 0.58316 | \n", "
8 | \n", "Summary | \n", "Statistics | \n", "Transformed | \n", "# Distinct Values | \n", "\n", " | 118.0 | \n", "
9 | \n", "White Noise | \n", "Ljung-Box | \n", "Transformed | \n", "Test Statictic | \n", "{'alpha': 0.05, 'K': 24} | \n", "1606.083817 | \n", "
10 | \n", "White Noise | \n", "Ljung-Box | \n", "Transformed | \n", "Test Statictic | \n", "{'alpha': 0.05, 'K': 48} | \n", "1933.155822 | \n", "
11 | \n", "White Noise | \n", "Ljung-Box | \n", "Transformed | \n", "p-value | \n", "{'alpha': 0.05, 'K': 24} | \n", "0.0 | \n", "
12 | \n", "White Noise | \n", "Ljung-Box | \n", "Transformed | \n", "p-value | \n", "{'alpha': 0.05, 'K': 48} | \n", "0.0 | \n", "
13 | \n", "White Noise | \n", "Ljung-Box | \n", "Transformed | \n", "White Noise | \n", "{'alpha': 0.05, 'K': 24} | \n", "False | \n", "
14 | \n", "White Noise | \n", "Ljung-Box | \n", "Transformed | \n", "White Noise | \n", "{'alpha': 0.05, 'K': 48} | \n", "False | \n", "
15 | \n", "Stationarity | \n", "ADF | \n", "Transformed | \n", "Stationarity | \n", "{'alpha': 0.05} | \n", "False | \n", "
16 | \n", "Stationarity | \n", "ADF | \n", "Transformed | \n", "p-value | \n", "{'alpha': 0.05} | \n", "0.99188 | \n", "
17 | \n", "Stationarity | \n", "ADF | \n", "Transformed | \n", "Test Statistic | \n", "{'alpha': 0.05} | \n", "0.815369 | \n", "
18 | \n", "Stationarity | \n", "ADF | \n", "Transformed | \n", "Critical Value 1% | \n", "{'alpha': 0.05} | \n", "-3.481682 | \n", "
19 | \n", "Stationarity | \n", "ADF | \n", "Transformed | \n", "Critical Value 5% | \n", "{'alpha': 0.05} | \n", "-2.884042 | \n", "
20 | \n", "Stationarity | \n", "ADF | \n", "Transformed | \n", "Critical Value 10% | \n", "{'alpha': 0.05} | \n", "-2.57877 | \n", "
21 | \n", "Stationarity | \n", "KPSS | \n", "Transformed | \n", "Trend Stationarity | \n", "{'alpha': 0.05} | \n", "True | \n", "
22 | \n", "Stationarity | \n", "KPSS | \n", "Transformed | \n", "p-value | \n", "{'alpha': 0.05} | \n", "0.1 | \n", "
23 | \n", "Stationarity | \n", "KPSS | \n", "Transformed | \n", "Test Statistic | \n", "{'alpha': 0.05} | \n", "0.09615 | \n", "
24 | \n", "Stationarity | \n", "KPSS | \n", "Transformed | \n", "Critical Value 10% | \n", "{'alpha': 0.05} | \n", "0.119 | \n", "
25 | \n", "Stationarity | \n", "KPSS | \n", "Transformed | \n", "Critical Value 5% | \n", "{'alpha': 0.05} | \n", "0.146 | \n", "
26 | \n", "Stationarity | \n", "KPSS | \n", "Transformed | \n", "Critical Value 2.5% | \n", "{'alpha': 0.05} | \n", "0.176 | \n", "
27 | \n", "Stationarity | \n", "KPSS | \n", "Transformed | \n", "Critical Value 1% | \n", "{'alpha': 0.05} | \n", "0.216 | \n", "
28 | \n", "Normality | \n", "Shapiro | \n", "Transformed | \n", "Normality | \n", "{'alpha': 0.05} | \n", "False | \n", "
29 | \n", "Normality | \n", "Shapiro | \n", "Transformed | \n", "p-value | \n", "{'alpha': 0.05} | \n", "0.000068 | \n", "
\n", " | Test | \n", "Test Name | \n", "Data | \n", "Property | \n", "Setting | \n", "Value | \n", "
---|---|---|---|---|---|---|
0 | \n", "Summary | \n", "Statistics | \n", "Residual | \n", "Length | \n", "\n", " | 141.0 | \n", "
1 | \n", "Summary | \n", "Statistics | \n", "Residual | \n", "# Missing Values | \n", "\n", " | 0.0 | \n", "
2 | \n", "Summary | \n", "Statistics | \n", "Residual | \n", "Mean | \n", "\n", " | -0.040771 | \n", "
3 | \n", "Summary | \n", "Statistics | \n", "Residual | \n", "Median | \n", "\n", " | -0.9734 | \n", "
4 | \n", "Summary | \n", "Statistics | \n", "Residual | \n", "Standard Deviation | \n", "\n", " | 10.584861 | \n", "
5 | \n", "Summary | \n", "Statistics | \n", "Residual | \n", "Variance | \n", "\n", " | 112.039291 | \n", "
6 | \n", "Summary | \n", "Statistics | \n", "Residual | \n", "Kurtosis | \n", "\n", " | 1.564477 | \n", "
7 | \n", "Summary | \n", "Statistics | \n", "Residual | \n", "Skewness | \n", "\n", " | -0.180433 | \n", "
8 | \n", "Summary | \n", "Statistics | \n", "Residual | \n", "# Distinct Values | \n", "\n", " | 141.0 | \n", "
9 | \n", "White Noise | \n", "Ljung-Box | \n", "Residual | \n", "Test Statictic | \n", "{'alpha': 0.05, 'K': 24} | \n", "41.377235 | \n", "
10 | \n", "White Noise | \n", "Ljung-Box | \n", "Residual | \n", "Test Statictic | \n", "{'alpha': 0.05, 'K': 48} | \n", "62.234507 | \n", "
11 | \n", "White Noise | \n", "Ljung-Box | \n", "Residual | \n", "p-value | \n", "{'alpha': 0.05, 'K': 24} | \n", "0.015137 | \n", "
12 | \n", "White Noise | \n", "Ljung-Box | \n", "Residual | \n", "p-value | \n", "{'alpha': 0.05, 'K': 48} | \n", "0.081294 | \n", "
13 | \n", "White Noise | \n", "Ljung-Box | \n", "Residual | \n", "White Noise | \n", "{'alpha': 0.05, 'K': 24} | \n", "False | \n", "
14 | \n", "White Noise | \n", "Ljung-Box | \n", "Residual | \n", "White Noise | \n", "{'alpha': 0.05, 'K': 48} | \n", "True | \n", "
15 | \n", "Stationarity | \n", "ADF | \n", "Residual | \n", "Stationarity | \n", "{'alpha': 0.05} | \n", "True | \n", "
16 | \n", "Stationarity | \n", "ADF | \n", "Residual | \n", "p-value | \n", "{'alpha': 0.05} | \n", "0.000377 | \n", "
17 | \n", "Stationarity | \n", "ADF | \n", "Residual | \n", "Test Statistic | \n", "{'alpha': 0.05} | \n", "-4.341183 | \n", "
18 | \n", "Stationarity | \n", "ADF | \n", "Residual | \n", "Critical Value 1% | \n", "{'alpha': 0.05} | \n", "-3.481282 | \n", "
19 | \n", "Stationarity | \n", "ADF | \n", "Residual | \n", "Critical Value 5% | \n", "{'alpha': 0.05} | \n", "-2.883868 | \n", "
20 | \n", "Stationarity | \n", "ADF | \n", "Residual | \n", "Critical Value 10% | \n", "{'alpha': 0.05} | \n", "-2.578677 | \n", "
21 | \n", "Stationarity | \n", "KPSS | \n", "Residual | \n", "Trend Stationarity | \n", "{'alpha': 0.05} | \n", "True | \n", "
22 | \n", "Stationarity | \n", "KPSS | \n", "Residual | \n", "p-value | \n", "{'alpha': 0.05} | \n", "0.1 | \n", "
23 | \n", "Stationarity | \n", "KPSS | \n", "Residual | \n", "Test Statistic | \n", "{'alpha': 0.05} | \n", "0.036131 | \n", "
24 | \n", "Stationarity | \n", "KPSS | \n", "Residual | \n", "Critical Value 10% | \n", "{'alpha': 0.05} | \n", "0.119 | \n", "
25 | \n", "Stationarity | \n", "KPSS | \n", "Residual | \n", "Critical Value 5% | \n", "{'alpha': 0.05} | \n", "0.146 | \n", "
26 | \n", "Stationarity | \n", "KPSS | \n", "Residual | \n", "Critical Value 2.5% | \n", "{'alpha': 0.05} | \n", "0.176 | \n", "
27 | \n", "Stationarity | \n", "KPSS | \n", "Residual | \n", "Critical Value 1% | \n", "{'alpha': 0.05} | \n", "0.216 | \n", "
28 | \n", "Normality | \n", "Shapiro | \n", "Residual | \n", "Normality | \n", "{'alpha': 0.05} | \n", "False | \n", "
29 | \n", "Normality | \n", "Shapiro | \n", "Residual | \n", "p-value | \n", "{'alpha': 0.05} | \n", "0.026076 | \n", "
\n", " | Name | \n", "Reference | \n", "Turbo | \n", "
---|---|---|---|
ID | \n", "\n", " | \n", " | \n", " |
naive | \n", "Naive Forecaster | \n", "sktime.forecasting.naive.NaiveForecaster | \n", "True | \n", "
grand_means | \n", "Grand Means Forecaster | \n", "sktime.forecasting.naive.NaiveForecaster | \n", "True | \n", "
snaive | \n", "Seasonal Naive Forecaster | \n", "sktime.forecasting.naive.NaiveForecaster | \n", "True | \n", "
polytrend | \n", "Polynomial Trend Forecaster | \n", "sktime.forecasting.trend.PolynomialTrendForeca... | \n", "True | \n", "
arima | \n", "ARIMA | \n", "sktime.forecasting.arima.ARIMA | \n", "True | \n", "
auto_arima | \n", "Auto ARIMA | \n", "sktime.forecasting.arima.AutoARIMA | \n", "True | \n", "
exp_smooth | \n", "Exponential Smoothing | \n", "sktime.forecasting.exp_smoothing.ExponentialSm... | \n", "True | \n", "
croston | \n", "Croston | \n", "sktime.forecasting.croston.Croston | \n", "True | \n", "
ets | \n", "ETS | \n", "sktime.forecasting.ets.AutoETS | \n", "True | \n", "
theta | \n", "Theta Forecaster | \n", "sktime.forecasting.theta.ThetaForecaster | \n", "True | \n", "
tbats | \n", "TBATS | \n", "sktime.forecasting.tbats.TBATS | \n", "False | \n", "
bats | \n", "BATS | \n", "sktime.forecasting.bats.BATS | \n", "False | \n", "
lr_cds_dt | \n", "Linear w/ Cond. Deseasonalize & Detrending | \n", "pycaret.containers.models.time_series.BaseCdsD... | \n", "True | \n", "
en_cds_dt | \n", "Elastic Net w/ Cond. Deseasonalize & Detrending | \n", "pycaret.containers.models.time_series.BaseCdsD... | \n", "True | \n", "
ridge_cds_dt | \n", "Ridge w/ Cond. Deseasonalize & Detrending | \n", "pycaret.containers.models.time_series.BaseCdsD... | \n", "True | \n", "
lasso_cds_dt | \n", "Lasso w/ Cond. Deseasonalize & Detrending | \n", "pycaret.containers.models.time_series.BaseCdsD... | \n", "True | \n", "
lar_cds_dt | \n", "Least Angular Regressor w/ Cond. Deseasonalize... | \n", "pycaret.containers.models.time_series.BaseCdsD... | \n", "True | \n", "
llar_cds_dt | \n", "Lasso Least Angular Regressor w/ Cond. Deseaso... | \n", "pycaret.containers.models.time_series.BaseCdsD... | \n", "True | \n", "
br_cds_dt | \n", "Bayesian Ridge w/ Cond. Deseasonalize & Detren... | \n", "pycaret.containers.models.time_series.BaseCdsD... | \n", "True | \n", "
huber_cds_dt | \n", "Huber w/ Cond. Deseasonalize & Detrending | \n", "pycaret.containers.models.time_series.BaseCdsD... | \n", "True | \n", "
par_cds_dt | \n", "Passive Aggressive w/ Cond. Deseasonalize & De... | \n", "pycaret.containers.models.time_series.BaseCdsD... | \n", "True | \n", "
omp_cds_dt | \n", "Orthogonal Matching Pursuit w/ Cond. Deseasona... | \n", "pycaret.containers.models.time_series.BaseCdsD... | \n", "True | \n", "
knn_cds_dt | \n", "K Neighbors w/ Cond. Deseasonalize & Detrending | \n", "pycaret.containers.models.time_series.BaseCdsD... | \n", "True | \n", "
dt_cds_dt | \n", "Decision Tree w/ Cond. Deseasonalize & Detrending | \n", "pycaret.containers.models.time_series.BaseCdsD... | \n", "True | \n", "
rf_cds_dt | \n", "Random Forest w/ Cond. Deseasonalize & Detrending | \n", "pycaret.containers.models.time_series.BaseCdsD... | \n", "True | \n", "
et_cds_dt | \n", "Extra Trees w/ Cond. Deseasonalize & Detrending | \n", "pycaret.containers.models.time_series.BaseCdsD... | \n", "True | \n", "
gbr_cds_dt | \n", "Gradient Boosting w/ Cond. Deseasonalize & Det... | \n", "pycaret.containers.models.time_series.BaseCdsD... | \n", "True | \n", "
ada_cds_dt | \n", "AdaBoost w/ Cond. Deseasonalize & Detrending | \n", "pycaret.containers.models.time_series.BaseCdsD... | \n", "True | \n", "
xgboost_cds_dt | \n", "Extreme Gradient Boosting w/ Cond. Deseasonali... | \n", "pycaret.containers.models.time_series.BaseCdsD... | \n", "True | \n", "
lightgbm_cds_dt | \n", "Light Gradient Boosting w/ Cond. Deseasonalize... | \n", "pycaret.containers.models.time_series.BaseCdsD... | \n", "True | \n", "
catboost_cds_dt | \n", "CatBoost Regressor w/ Cond. Deseasonalize & De... | \n", "pycaret.containers.models.time_series.BaseCdsD... | \n", "True | \n", "
\n", " | cutoff | \n", "MASE | \n", "RMSSE | \n", "MAE | \n", "RMSE | \n", "MAPE | \n", "SMAPE | \n", "R2 | \n", "
---|---|---|---|---|---|---|---|---|
0 | \n", "1959-12 | \n", "0.5083 | \n", "0.7238 | \n", "15.4772 | \n", "25.0045 | \n", "0.0371 | \n", "0.0354 | \n", "-2.8436 | \n", "
1 | \n", "1960-03 | \n", "0.6856 | \n", "0.6262 | \n", "21.0315 | \n", "21.7984 | \n", "0.0437 | \n", "0.0448 | \n", "0.5529 | \n", "
2 | \n", "1960-06 | \n", "0.2796 | \n", "0.3123 | \n", "8.7733 | \n", "11.1270 | \n", "0.0147 | \n", "0.0146 | \n", "0.9512 | \n", "
Mean | \n", "NaT | \n", "0.4912 | \n", "0.5541 | \n", "15.0940 | \n", "19.3099 | \n", "0.0318 | \n", "0.0316 | \n", "-0.4465 | \n", "
SD | \n", "NaT | \n", "0.1662 | \n", "0.1755 | \n", "5.0117 | \n", "5.9324 | \n", "0.0124 | \n", "0.0126 | \n", "1.7028 | \n", "
\n", " | cutoff | \n", "MASE | \n", "RMSSE | \n", "MAE | \n", "RMSE | \n", "MAPE | \n", "SMAPE | \n", "R2 | \n", "
---|---|---|---|---|---|---|---|---|
0 | \n", "1959-12 | \n", "0.5083 | \n", "0.7238 | \n", "15.4772 | \n", "25.0045 | \n", "0.0371 | \n", "0.0354 | \n", "-2.8436 | \n", "
1 | \n", "1960-03 | \n", "0.6856 | \n", "0.6262 | \n", "21.0315 | \n", "21.7984 | \n", "0.0437 | \n", "0.0448 | \n", "0.5529 | \n", "
2 | \n", "1960-06 | \n", "0.2796 | \n", "0.3123 | \n", "8.7733 | \n", "11.1270 | \n", "0.0147 | \n", "0.0146 | \n", "0.9512 | \n", "
Mean | \n", "NaT | \n", "0.4912 | \n", "0.5541 | \n", "15.0940 | \n", "19.3099 | \n", "0.0318 | \n", "0.0316 | \n", "-0.4465 | \n", "
SD | \n", "NaT | \n", "0.1662 | \n", "0.1755 | \n", "5.0117 | \n", "5.9324 | \n", "0.0124 | \n", "0.0126 | \n", "1.7028 | \n", "
\n", " | cutoff | \n", "MASE | \n", "RMSSE | \n", "MAE | \n", "RMSE | \n", "MAPE | \n", "SMAPE | \n", "R2 | \n", "
---|---|---|---|---|---|---|---|---|
0 | \n", "1959-06 | \n", "0.8152 | \n", "0.8212 | \n", "23.7114 | \n", "27.0777 | \n", "0.0436 | \n", "0.0448 | \n", "0.6016 | \n", "
1 | \n", "1959-09 | \n", "0.1622 | \n", "0.1723 | \n", "4.8339 | \n", "5.8216 | \n", "0.0127 | \n", "0.0128 | \n", "0.9213 | \n", "
2 | \n", "1959-12 | \n", "0.6788 | \n", "0.7857 | \n", "20.6700 | \n", "27.1432 | \n", "0.0501 | \n", "0.0481 | \n", "-3.5292 | \n", "
3 | \n", "1960-03 | \n", "2.0377 | \n", "1.8037 | \n", "62.5075 | \n", "62.7874 | \n", "0.1276 | \n", "0.1363 | \n", "-2.7090 | \n", "
4 | \n", "1960-06 | \n", "0.5352 | \n", "0.5287 | \n", "16.7895 | \n", "18.8359 | \n", "0.0282 | \n", "0.0286 | \n", "0.8603 | \n", "
Mean | \n", "NaT | \n", "0.8458 | \n", "0.8223 | \n", "25.7024 | \n", "28.3332 | \n", "0.0524 | \n", "0.0541 | \n", "-0.7710 | \n", "
SD | \n", "NaT | \n", "0.6346 | \n", "0.5428 | \n", "19.4876 | \n", "18.9053 | \n", "0.0397 | \n", "0.0430 | \n", "1.9377 | \n", "
\n", " | cutoff | \n", "MASE | \n", "RMSSE | \n", "MAE | \n", "RMSE | \n", "MAPE | \n", "SMAPE | \n", "R2 | \n", "
---|---|---|---|---|---|---|---|---|
0 | \n", "1959-06 | \n", "1.9597 | \n", "1.9658 | \n", "57.0033 | \n", "64.8214 | \n", "0.1046 | \n", "0.1117 | \n", "-1.2833 | \n", "
1 | \n", "1959-09 | \n", "2.5537 | \n", "2.3345 | \n", "76.0868 | \n", "78.8857 | \n", "0.1979 | \n", "0.1785 | \n", "-13.4421 | \n", "
2 | \n", "1959-12 | \n", "0.3980 | \n", "0.3686 | \n", "12.1206 | \n", "12.7351 | \n", "0.0300 | \n", "0.0298 | \n", "0.0030 | \n", "
3 | \n", "1960-03 | \n", "2.1688 | \n", "2.1163 | \n", "66.5262 | \n", "73.6688 | \n", "0.1324 | \n", "0.1436 | \n", "-4.1060 | \n", "
4 | \n", "1960-06 | \n", "1.9552 | \n", "1.8291 | \n", "61.3391 | \n", "65.1682 | \n", "0.1034 | \n", "0.1083 | \n", "-0.6723 | \n", "
Mean | \n", "NaT | \n", "1.8071 | \n", "1.7229 | \n", "54.6152 | \n", "59.0559 | \n", "0.1136 | \n", "0.1144 | \n", "-3.9002 | \n", "
SD | \n", "NaT | \n", "0.7374 | \n", "0.6976 | \n", "22.1793 | \n", "23.7612 | \n", "0.0541 | \n", "0.0493 | \n", "4.9718 | \n", "
ThetaForecaster(deseasonalize=False, sp=12)In a Jupyter environment, please rerun this cell to show the HTML representation or trust the notebook.
ThetaForecaster(deseasonalize=False, sp=12)
\n", " | cutoff | \n", "MASE | \n", "RMSSE | \n", "MAE | \n", "RMSE | \n", "MAPE | \n", "SMAPE | \n", "R2 | \n", "
---|---|---|---|---|---|---|---|---|
0 | \n", "1959-12 | \n", "0.5039 | \n", "0.5459 | \n", "15.3434 | \n", "18.8593 | \n", "0.0377 | \n", "0.0388 | \n", "-1.1865 | \n", "
1 | \n", "1960-03 | \n", "1.5566 | \n", "1.3747 | \n", "47.7489 | \n", "47.8526 | \n", "0.0984 | \n", "0.1036 | \n", "-1.1544 | \n", "
2 | \n", "1960-06 | \n", "1.5185 | \n", "1.4832 | \n", "47.6395 | \n", "52.8433 | \n", "0.0838 | \n", "0.0884 | \n", "-0.0996 | \n", "
Mean | \n", "NaT | \n", "1.1930 | \n", "1.1346 | \n", "36.9106 | \n", "39.8518 | \n", "0.0733 | \n", "0.0769 | \n", "-0.8135 | \n", "
SD | \n", "NaT | \n", "0.4875 | \n", "0.4186 | \n", "15.2504 | \n", "14.9831 | \n", "0.0259 | \n", "0.0277 | \n", "0.5050 | \n", "
\n", " | cutoff | \n", "MASE | \n", "RMSSE | \n", "MAE | \n", "RMSE | \n", "MAPE | \n", "SMAPE | \n", "R2 | \n", "
---|---|---|---|---|---|---|---|---|
0 | \n", "1959-12 | \n", "0.6369 | \n", "0.7822 | \n", "19.3938 | \n", "27.0225 | \n", "0.0470 | \n", "0.0450 | \n", "-3.4890 | \n", "
1 | \n", "1960-03 | \n", "1.3005 | \n", "1.1639 | \n", "39.8938 | \n", "40.5155 | \n", "0.0819 | \n", "0.0856 | \n", "-0.5444 | \n", "
2 | \n", "1960-06 | \n", "0.9561 | \n", "0.9788 | \n", "29.9971 | \n", "34.8742 | \n", "0.0495 | \n", "0.0512 | \n", "0.5211 | \n", "
Mean | \n", "NaT | \n", "0.9645 | \n", "0.9750 | \n", "29.7616 | \n", "34.1374 | \n", "0.0595 | \n", "0.0606 | \n", "-1.1708 | \n", "
SD | \n", "NaT | \n", "0.2710 | \n", "0.1559 | \n", "8.3707 | \n", "5.5331 | \n", "0.0159 | \n", "0.0178 | \n", "1.6960 | \n", "
BaseCdsDtForecaster(fe_target_rr=[WindowSummarizer(lag_feature={'lag': [12, 11,\n", " 10, 9,\n", " 8, 7, 6,\n", " 5, 4, 3,\n", " 2, 1]},\n", " n_jobs=1)],\n", " regressor=DecisionTreeRegressor(random_state=123), sp=12,\n", " window_length=12)In a Jupyter environment, please rerun this cell to show the HTML representation or trust the notebook.
BaseCdsDtForecaster(fe_target_rr=[WindowSummarizer(lag_feature={'lag': [12, 11,\n", " 10, 9,\n", " 8, 7, 6,\n", " 5, 4, 3,\n", " 2, 1]},\n", " n_jobs=1)],\n", " regressor=DecisionTreeRegressor(random_state=123), sp=12,\n", " window_length=12)
DecisionTreeRegressor(random_state=123)
DecisionTreeRegressor(random_state=123)
\n", " | cutoff | \n", "MASE | \n", "RMSSE | \n", "MAE | \n", "RMSE | \n", "MAPE | \n", "SMAPE | \n", "R2 | \n", "
---|---|---|---|---|---|---|---|---|
0 | \n", "1959-12 | \n", "0.5466 | \n", "0.5815 | \n", "16.6450 | \n", "20.0910 | \n", "0.0409 | \n", "0.0421 | \n", "-1.4814 | \n", "
1 | \n", "1960-03 | \n", "1.2777 | \n", "1.1388 | \n", "39.1945 | \n", "39.6419 | \n", "0.0799 | \n", "0.0833 | \n", "-0.4785 | \n", "
2 | \n", "1960-06 | \n", "1.6742 | \n", "1.5262 | \n", "52.5234 | \n", "54.3772 | \n", "0.0906 | \n", "0.0952 | \n", "-0.1643 | \n", "
Mean | \n", "NaT | \n", "1.1662 | \n", "1.0822 | \n", "36.1210 | \n", "38.0367 | \n", "0.0705 | \n", "0.0735 | \n", "-0.7081 | \n", "
SD | \n", "NaT | \n", "0.4670 | \n", "0.3877 | \n", "14.8077 | \n", "14.0432 | \n", "0.0214 | \n", "0.0227 | \n", "0.5617 | \n", "
BaseCdsDtForecaster(fe_target_rr=[WindowSummarizer(lag_feature={'lag': [12, 11,\n", " 10, 9,\n", " 8, 7, 6,\n", " 5, 4, 3,\n", " 2, 1]},\n", " n_jobs=1)],\n", " regressor=DecisionTreeRegressor(max_depth=4,\n", " random_state=123),\n", " sp=12, window_length=12)In a Jupyter environment, please rerun this cell to show the HTML representation or trust the notebook.
BaseCdsDtForecaster(fe_target_rr=[WindowSummarizer(lag_feature={'lag': [12, 11,\n", " 10, 9,\n", " 8, 7, 6,\n", " 5, 4, 3,\n", " 2, 1]},\n", " n_jobs=1)],\n", " regressor=DecisionTreeRegressor(max_depth=4,\n", " random_state=123),\n", " sp=12, window_length=12)
DecisionTreeRegressor(max_depth=4, random_state=123)
DecisionTreeRegressor(max_depth=4, random_state=123)
\n", " | cutoff | \n", "MASE | \n", "RMSSE | \n", "MAE | \n", "RMSE | \n", "MAPE | \n", "SMAPE | \n", "R2 | \n", "
---|---|---|---|---|---|---|---|---|
0 | \n", "1959-12 | \n", "0.6369 | \n", "0.7822 | \n", "19.3938 | \n", "27.0225 | \n", "0.0470 | \n", "0.0450 | \n", "-3.4890 | \n", "
1 | \n", "1960-03 | \n", "1.3005 | \n", "1.1639 | \n", "39.8938 | \n", "40.5155 | \n", "0.0819 | \n", "0.0856 | \n", "-0.5444 | \n", "
2 | \n", "1960-06 | \n", "0.9561 | \n", "0.9788 | \n", "29.9971 | \n", "34.8742 | \n", "0.0495 | \n", "0.0512 | \n", "0.5211 | \n", "
Mean | \n", "NaT | \n", "0.9645 | \n", "0.9750 | \n", "29.7616 | \n", "34.1374 | \n", "0.0595 | \n", "0.0606 | \n", "-1.1708 | \n", "
SD | \n", "NaT | \n", "0.2710 | \n", "0.1559 | \n", "8.3707 | \n", "5.5331 | \n", "0.0159 | \n", "0.0178 | \n", "1.6960 | \n", "
BaseCdsDtForecaster(degree=3, deseasonal_model='multiplicative',\n", " fe_target_rr=[WindowSummarizer(lag_feature={'lag': [12, 11,\n", " 10, 9,\n", " 8, 7, 6,\n", " 5, 4, 3,\n", " 2, 1]},\n", " n_jobs=1)],\n", " regressor=DecisionTreeRegressor(max_depth=9,\n", " max_features='log2',\n", " min_impurity_decrease=0.005742993267225779,\n", " min_samples_leaf=5,\n", " min_samples_split=4,\n", " random_state=123),\n", " sp=12, window_length=22)In a Jupyter environment, please rerun this cell to show the HTML representation or trust the notebook.
BaseCdsDtForecaster(degree=3, deseasonal_model='multiplicative',\n", " fe_target_rr=[WindowSummarizer(lag_feature={'lag': [12, 11,\n", " 10, 9,\n", " 8, 7, 6,\n", " 5, 4, 3,\n", " 2, 1]},\n", " n_jobs=1)],\n", " regressor=DecisionTreeRegressor(max_depth=9,\n", " max_features='log2',\n", " min_impurity_decrease=0.005742993267225779,\n", " min_samples_leaf=5,\n", " min_samples_split=4,\n", " random_state=123),\n", " sp=12, window_length=22)
DecisionTreeRegressor(max_depth=9, max_features='log2',\n", " min_impurity_decrease=0.005742993267225779,\n", " min_samples_leaf=5, min_samples_split=4,\n", " random_state=123)
DecisionTreeRegressor(max_depth=9, max_features='log2',\n", " min_impurity_decrease=0.005742993267225779,\n", " min_samples_leaf=5, min_samples_split=4,\n", " random_state=123)
\n", " | cutoff | \n", "MASE | \n", "RMSSE | \n", "MAE | \n", "RMSE | \n", "MAPE | \n", "SMAPE | \n", "R2 | \n", "
---|---|---|---|---|---|---|---|---|
0 | \n", "1959-12 | \n", "0.1240 | \n", "0.1641 | \n", "3.7761 | \n", "5.6693 | \n", "0.0091 | \n", "0.0092 | \n", "0.8024 | \n", "
1 | \n", "1960-03 | \n", "1.4150 | \n", "1.2555 | \n", "43.4050 | \n", "43.7064 | \n", "0.0890 | \n", "0.0932 | \n", "-0.7972 | \n", "
2 | \n", "1960-06 | \n", "0.7444 | \n", "0.7505 | \n", "23.3552 | \n", "26.7403 | \n", "0.0386 | \n", "0.0395 | \n", "0.7184 | \n", "
Mean | \n", "NaT | \n", "0.7612 | \n", "0.7234 | \n", "23.5121 | \n", "25.3720 | \n", "0.0456 | \n", "0.0473 | \n", "0.2412 | \n", "
SD | \n", "NaT | \n", "0.5272 | \n", "0.4460 | \n", "16.1788 | \n", "15.5587 | \n", "0.0330 | \n", "0.0347 | \n", "0.7351 | \n", "
_EnsembleForecasterWithVoting(forecasters=[('HuberRegressor',\n", " BaseCdsDtForecaster(fe_target_rr=[WindowSummarizer(lag_feature={'lag': [12,\n", " 11,\n", " 10,\n", " 9,\n", " 8,\n", " 7,\n", " 6,\n", " 5,\n", " 4,\n", " 3,\n", " 2,\n", " 1]},\n", " n_jobs=1)],\n", " regressor=HuberRegressor(),\n", " sp=12,\n", " window_length=12)),\n", " ('Lars',\n", " BaseCdsDtForecaster(fe_target_rr=[WindowSummarizer(lag_feature={'lag': [12,\n", " 11,\n", " 10,\n", " 9,\n", " 8,\n", " 7,\n", " 6,\n", " 5,\n", " 4,\n", " 3,\n", " 2,\n", " 1]},\n", " n_jobs=1)],\n", " regressor=Lars(random_state=123),\n", " sp=12,\n", " window_length=12)),\n", " ('PassiveAggressiveRegressor',\n", " BaseCdsDtForecaster(fe_target_rr=[WindowSummarizer(lag_feature={'lag': [12,\n", " 11,\n", " 10,\n", " 9,\n", " 8,\n", " 7,\n", " 6,\n", " 5,\n", " 4,\n", " 3,\n", " 2,\n", " 1]},\n", " n_jobs=1)],\n", " regressor=PassiveAggressiveRegressor(random_state=123),\n", " sp=12,\n", " window_length=12))],\n", " n_jobs=-1)In a Jupyter environment, please rerun this cell to show the HTML representation or trust the notebook.
_EnsembleForecasterWithVoting(forecasters=[('HuberRegressor',\n", " BaseCdsDtForecaster(fe_target_rr=[WindowSummarizer(lag_feature={'lag': [12,\n", " 11,\n", " 10,\n", " 9,\n", " 8,\n", " 7,\n", " 6,\n", " 5,\n", " 4,\n", " 3,\n", " 2,\n", " 1]},\n", " n_jobs=1)],\n", " regressor=HuberRegressor(),\n", " sp=12,\n", " window_length=12)),\n", " ('Lars',\n", " BaseCdsDtForecaster(fe_target_rr=[WindowSummarizer(lag_feature={'lag': [12,\n", " 11,\n", " 10,\n", " 9,\n", " 8,\n", " 7,\n", " 6,\n", " 5,\n", " 4,\n", " 3,\n", " 2,\n", " 1]},\n", " n_jobs=1)],\n", " regressor=Lars(random_state=123),\n", " sp=12,\n", " window_length=12)),\n", " ('PassiveAggressiveRegressor',\n", " BaseCdsDtForecaster(fe_target_rr=[WindowSummarizer(lag_feature={'lag': [12,\n", " 11,\n", " 10,\n", " 9,\n", " 8,\n", " 7,\n", " 6,\n", " 5,\n", " 4,\n", " 3,\n", " 2,\n", " 1]},\n", " n_jobs=1)],\n", " regressor=PassiveAggressiveRegressor(random_state=123),\n", " sp=12,\n", " window_length=12))],\n", " n_jobs=-1)
ForecastingPipeline(steps=[('forecaster',\n", " TransformedTargetForecaster(steps=[('transformer_target',\n", " TransformerPipeline(steps=[('numerical_imputer',\n", " Imputer(random_state=123))])),\n", " ('model',\n", " AutoETS(seasonal='mul',\n", " sp=12,\n", " trend='add'))]))])In a Jupyter environment, please rerun this cell to show the HTML representation or trust the notebook.
ForecastingPipeline(steps=[('forecaster',\n", " TransformedTargetForecaster(steps=[('transformer_target',\n", " TransformerPipeline(steps=[('numerical_imputer',\n", " Imputer(random_state=123))])),\n", " ('model',\n", " AutoETS(seasonal='mul',\n", " sp=12,\n", " trend='add'))]))])
AutoETS(seasonal='mul', sp=12, trend='add')In a Jupyter environment, please rerun this cell to show the HTML representation or trust the notebook.
AutoETS(seasonal='mul', sp=12, trend='add')
\n", " | Description | \n", "Value | \n", "
---|---|---|
0 | \n", "session_id | \n", "123 | \n", "
1 | \n", "Target | \n", "Number of airline passengers | \n", "
2 | \n", "Approach | \n", "Univariate | \n", "
3 | \n", "Exogenous Variables | \n", "Not Present | \n", "
4 | \n", "Original data shape | \n", "(144, 1) | \n", "
5 | \n", "Transformed data shape | \n", "(144, 1) | \n", "
6 | \n", "Transformed train set shape | \n", "(141, 1) | \n", "
7 | \n", "Transformed test set shape | \n", "(3, 1) | \n", "
8 | \n", "Rows with missing values | \n", "0.0% | \n", "
9 | \n", "Fold Generator | \n", "ExpandingWindowSplitter | \n", "
10 | \n", "Fold Number | \n", "3 | \n", "
11 | \n", "Enforce Prediction Interval | \n", "False | \n", "
12 | \n", "Splits used for hyperparameters | \n", "all | \n", "
13 | \n", "Seasonality Detection Algo | \n", "auto | \n", "
14 | \n", "Max Period to Consider | \n", "60 | \n", "
15 | \n", "Seasonal Period(s) Tested | \n", "[12, 24, 36, 11, 48] | \n", "
16 | \n", "Significant Seasonal Period(s) | \n", "[12, 24, 36, 11, 48] | \n", "
17 | \n", "Significant Seasonal Period(s) without Harmonics | \n", "[48, 36, 11] | \n", "
18 | \n", "Remove Harmonics | \n", "False | \n", "
19 | \n", "Harmonics Order Method | \n", "harmonic_max | \n", "
20 | \n", "Num Seasonalities to Use | \n", "1 | \n", "
21 | \n", "All Seasonalities to Use | \n", "[12] | \n", "
22 | \n", "Primary Seasonality | \n", "12 | \n", "
23 | \n", "Seasonality Present | \n", "True | \n", "
24 | \n", "Target Strictly Positive | \n", "True | \n", "
25 | \n", "Target White Noise | \n", "No | \n", "
26 | \n", "Recommended d | \n", "1 | \n", "
27 | \n", "Recommended Seasonal D | \n", "1 | \n", "
28 | \n", "Preprocess | \n", "True | \n", "
29 | \n", "Numerical Imputation (Target) | \n", "drift | \n", "
30 | \n", "Transformation (Target) | \n", "None | \n", "
31 | \n", "Scaling (Target) | \n", "None | \n", "
32 | \n", "Feature Engineering (Target) - Reduced Regression | \n", "False | \n", "
33 | \n", "CPU Jobs | \n", "-1 | \n", "
34 | \n", "Use GPU | \n", "False | \n", "
35 | \n", "Log Experiment | \n", "False | \n", "
36 | \n", "Experiment Name | \n", "ts-default-name | \n", "
37 | \n", "USI | \n", "46d6 | \n", "