State space reconstruction from noisy nonlinear time series: An autoencoder-based approach
Date of Original Version
State space reconstruction is usually the first step of nonlinear time series analysis. Among many state space reconstruction approaches, the method of delays (MOD) has been a popular method in noise-free situations. Unfortunately, many real-world time series are usually noisy so that the reconstruction performance can be of low quality. In this paper, we propose an autoencoder-based approach that aims to reconstruct a high-quality state space from noisy nonlinear time series. We present the approach in detail and applied it to several typical nonlinear time series. The simulation results demonstrate that our method can generate better reconstructions than other popular approaches including MOD and principal component analysis (PCA).
Proceedings of the International Joint Conference on Neural Networks
Jiang, He, and Haibo He. "State space reconstruction from noisy nonlinear time series: An autoencoder-based approach." Proceedings of the International Joint Conference on Neural Networks 2017-May, (2017): 3191-3198. doi:10.1109/IJCNN.2017.7966254.