Gone in 30 days! Predictions for car import planning
| dc.contributor.author | Lacic, Emanuel | |
| dc.contributor.author | Traub, Matthias | |
| dc.contributor.author | Duricic, Tomislav | |
| dc.contributor.author | Haslauer, Eva | |
| dc.contributor.author | Lex, Elisabeth | |
| dc.date.accessioned | 2021-06-21T10:12:43Z | |
| dc.date.available | 2021-06-21T10:12:43Z | |
| dc.date.issued | 2018 | |
| dc.description.abstract | A challenge for importers in the automobile industry is adjusting to rapidly changing market demands. In this work, we describe a practical study of car import planning based on the monthly car registrations in Austria. We model the task as a data driven forecasting problem and we implement four different prediction approaches. One utilizes a seasonal ARIMA model, while the other is based on LSTM-RNN and both compared to a linear and seasonal baselines. In our experiments, we evaluate the 33 different brands by predicting the number of registrations for the next month and for the year to come. | en |
| dc.identifier.doi | 10.1515/itit-2017-0040 | |
| dc.identifier.pissn | 2196-7032 | |
| dc.identifier.uri | https://dl.gi.de/handle/20.500.12116/36617 | |
| dc.language.iso | en | |
| dc.publisher | De Gruyter | |
| dc.relation.ispartof | it - Information Technology: Vol. 60, No. 4 | |
| dc.subject | Automotive industry | |
| dc.subject | Data-driven expert systems | |
| dc.subject | Car brand recommendation | |
| dc.subject | Linear methods | |
| dc.subject | Nonlinear methods | |
| dc.subject | Deep learning | |
| dc.subject | Customer demand | |
| dc.title | Gone in 30 days! Predictions for car import planning | en |
| dc.type | Text/Journal Article | |
| gi.citation.endPage | 228 | |
| gi.citation.publisherPlace | Berlin | |
| gi.citation.startPage | 219 |