High-speed multivariate time series prediction using Echo State Network

Abstract

Recently, time series analysis using machine learning has been actively carried out, and it has been applied in various fields. Real-time prediction is important in real-time data prediction such as stock and traffic conditions. Many time-series prediction models perform large-scale learning using a large amount of data, so computational costs are large and impractical. In this research, we propose a time-series prediction method using Echo State Network capable of rapid learning. It was confirmed experimental that the rapid and high-performance learning model can be constructed by applying Echo State Network to the multivariate time series.