| Rolling linear regression |
110371.9301 |
332.2227116 |
234.0703576 |
0.9403432378 |
7.636594873 |
5.86419415 |
| Rolling polynomial regression |
884630.4331 |
940.547943 |
393.5678591 |
0.4983602673 |
12.2984977 |
5.491194888 |
| kNN regressor |
708562.9645 |
841.7618217 |
583.6696106 |
0.6447619117 |
10.37620696 |
8.242460991 |
| Rolling kNN regressor |
69306.98241 |
263.2621933 |
187.9282248 |
0.9625391151 |
6.412249097 |
4.733349022 |
| Naive XGBoost |
469450.7669 |
685.1647735 |
498.7413995 |
0.7646408275 |
8.971157778 |
7.471825957 |
| Rolling XGBoost |
67758.72249 |
260.3050566 |
188.7835581 |
0.9633759599 |
6.501568004 |
4.831666041 |
| Neural networks (57-20-1) |
2173130.493 |
1474.154162 |
1322.964328 |
-0.08949911367 |
24.5471328 |
25.18931498 |
| Rolling neural networks |
264933.6857 |
514.7170928 |
322.0846974 |
0.8566008466 |
10.2374128 |
7.382278679 |
| Naive decision trees |
589915.7445 |
768.0597272 |
550.3918629 |
0.7042457031 |
10.05114227 |
8.073536906 |
| Rolling decision trees |
94968.42101 |
308.169468 |
216.5232416 |
0.9486689369 |
7.211369358 |
5.384585412 |
| AdaBoost (with DT) |
1197958.106 |
1094.512725 |
903.7533006 |
0.3994036256 |
18.42378427 |
16.25435846 |
| Rolling AdaBoost (with DT) |
262510.5105 |
512.3577954 |
379.1674719 |
0.8581113234 |
12.16986269 |
9.953531307 |
| Bagging regressor (with DT) |
399350.6124 |
631.9419375 |
460.7829264 |
0.7997855445 |
8.243560544 |
6.856250353 |
| Rolling bagging regressor (with DT) |
65762.08751 |
256.441197 |
184.4860468 |
0.9644551544 |
6.295581978 |
4.707222345 |