no code implementations • Proceedings of the Thirtieth International Florida Artificial Intelligence Research Society Conference 520 2017 • Sajitha Naduvil-Vadukootu, Rafal A. Angryk, Pete Riley
We propose a novel approach to combine state-of-the-art time series data processing methods, such as symbolic aggregate approximation (SAX), with very recently developed deep neural network architectures, such as deep recurrent neural networks (DRNN), for time series data modeling and prediction.