My previous predictions were fairly naive in that it only took previous values from each car make separately. However, other variables should be of importance (economic mostly) and could increase accuracy of predictions.
I've made new predictions based on a vector auto random forests. Like vector auto regressions, it models each time series with lagged values of itself and lag values of all other variables. The nice thing about using random forests, is that you don't necessarily need to worry about model specifications as the algorithm generally gives good predictions.
Below are predictions for December 2014 car sales.
Code
I've made new predictions based on a vector auto random forests. Like vector auto regressions, it models each time series with lagged values of itself and lag values of all other variables. The nice thing about using random forests, is that you don't necessarily need to worry about model specifications as the algorithm generally gives good predictions.
Below are predictions for December 2014 car sales.
Code
| Predicted Values for 2014-12-01 | |
| Acura | 638.471 |
| Audi | 714.237 |
| BMW | 1,347.554 |
| Buick | 782.536 |
| Cadillac | 580.159 |
| Chevrolet | 6,122.498 |
| Chrysler | 1,088.584 |
| Dodge | 1,796.103 |
| Ford | 7,841.763 |
| GMC | 1,898.404 |
| Honda | 4,646.172 |
| Hyundai | 2,195.227 |
| Infiniti | 498.734 |
| Jaguar | 49.113 |
| Jeep | 2,445.042 |
| Kia | 1,695.402 |
| Land.Rover | 153.189 |
| Lexus | 1,261.509 |
| Lincoln | 359.453 |
| Mazda | 900.577 |
| Mercedes.Benz | 1,530.815 |
| Mini | 214.853 |
| Mitsubishi | 263.551 |
| Nissan | 3,752.873 |
| Porsche | 178.117 |
| Subaru | 2,012.891 |
| Toyota | 6,486.288 |
| Volkswagen | 1,307.579 |
| Volvo | 144.812 |
Hi Sam,
ReplyDeleteCan you share the in-sample and out-of-sample error rates?
Regards,
Naveen