Comparison of actual car registrations (blue) with three prediction methods (baseline, LSTM, SARIMA) for different brands. Each brand presents unique forecasting challenges - high-volume fluctuations (Volkswagen), luxury seasonality (Porsche), and unpredictable patterns (Smart).
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.
Type
Publication
it - Information Technology
Tomislav Đuričić
Researcher / Machine Learning Engineer / Software Engineer
My research interests include social-based recommender systems, graph neural networks and user modeling.