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Conference Paper
Enhancing Environmental Data Forecasting Performance by Utilizing Multi-region Data with Hard-parameter sharing
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- Authors
- Jang-Ho Choi, Miyoung Jang, Taewhi Lee, Jongho Won, Jiyong Kim
- Issue Date
- 2020-12
- Citation
- International Conference on Internet (ICONI) 2020, pp.1-3
- Language
- English
- Type
- Conference Paper
- Abstract
- Although deep neural network models are capable of learning complex non-linear relationship between input and target data, they require a large amount of well-balanced data in order to reach high performance level. Unfortunately, such abundant situations are quite rare in practice that in environmental data forecasting, for instance, datasets are not only severely imbalanced, but also scarce. Hence, this paper presents a multi-headed deep-neural network model that can effectively learn multi-region datasets mitigating data imbalance and insufficiency. The proposed architecture learns common features from multiple regions in addition to region-specific features of the target. The experimental studies show that the proposed network improves prediction performance by utilizing additional multi-region data more effectively.
- KSP Keywords
- Balanced data, Common features, Data Forecasting, Data imbalance, Deep neural network(DNN), Experimental study, Forecasting performance, High performance, Non-linear relationship, Performance levels, Specific features