Evaluating Preprocessing Strategies for Time Series Prediction Using Deep Learning Architectures

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.

Time Series Time Series Prediction