Evaluating Preprocessing Strategies for Time Series Prediction Using Deep Learning Architectures

Abstract

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 data appear extensively in various scientific domains and industrial applications, yet the challenges in accurate modeling and prediction from such data remain open. Deep recurrent neural networks (DRNN) have been proposed as promising approaches to sequence prediction. We extend this research to the new challenge of the time series prediction space, building a system that effectively combines recurrent neural networks (RNN) with time series specific preprocessing techniques. Our experiments show comparisons of model performance with various data preprocessing techniques. We demonstrate that preprocessed inputs can steer us towards simpler (and therefore more computationally efficient) architectures of neural networks (when compared to original inputs).

Tasks

Reproductions