Percolative Learning: Time-Series Predictions from Future Tendencies

摘要

Multimodal learning has received considerable attention in recent years. Most prevailing approaches use multiple networks to learn a shared representation across modalities. However, these methods are limited to the laboratory scale because of unavailability of multimodal data. Percolative learning is a straightforward framework for solving this problem; we train our model using data from multimodalities. The model performs well during the testing phase, although only data from a single modality is provided. In this paper, we extend this framework to the problem of time-series prediction, specifically with focus on applications on maritime indices. Then, we attempt to exploit the potential of percolative learning. We also attempt to employ a genetic algorithm to find suitable architectures, which is one of the most important ingredients of percolative learning. The experimental results demonstrate the potential of our method and its superiority to other baselines.