ForeTiS-HortiCo is a Python framework that enables the rigorous training, comparison and analysis of time series forecasting for a variety of different models. ForeTiS includes multiple state-of-the-art prediction models or machine learning methods, respectively. These range from classical models, such as regularized linear regression over ensemble learners, e.g. XGBoost, to deep learning-based architectures, such as Multilayer Perceptron (MLP). To enable automatic hyperparameter optimization, we leverage state-of-the-art and efficient Bayesian optimization techniques. In addition, our framework is designed to allow an easy and straightforward integration of further prediction models.
This pipeline is developed and maintained by members of the Bioinformatics lab lead by Prof. Dr. Dominik Grimm:
Machine Learning Outperforms Classical Forecasting on Horticultural Sales Predictions. F Haselbeck, J Killinger, K Menrad, T Hannus, DG Grimm. Machine Learning with Applications, 2022 (https://doi.org/10.1016/j.mlwa.2021.100239)
A manuscript for publishing ForeTiS as a further scientific paper is currently under preparation.