Abstract
The focus of this work is the development of various hybrid prediction models, capable of predicting a given variable in the context of a time series with sporadic external stimuli. As a case study, we use data from a university student parking lot, together with other events and information. Working on top of previous research, we used Gradient Boosting models, Random Forests and Decision Trees in three proposed hybrid approaches: a voting-based combination of models, an approach based on pairs of models working together and a third novel approach, based on social dynamics and trust in human beings, called Evolutionary Directed Graph Ensemble (EDGE). Results show some promise from these methods, in particular from the EDGE approach.
The first author was supported by the Calouste Gulbenkian Foundation, under a New Talents in Artificial Intelligence Program Grant.
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Fontes, X., Castro Silva, D. (2020). Hybrid Approaches for Time Series Prediction. In: Madureira, A., Abraham, A., Gandhi, N., Varela, M. (eds) Hybrid Intelligent Systems. HIS 2018. Advances in Intelligent Systems and Computing, vol 923. Springer, Cham. https://doi.org/10.1007/978-3-030-14347-3_15
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