Title
Data-driven detrending of nonstationary fractal time series with echo state networks.
Abstract
In this paper, we propose a novel data-driven approach for removing trends (detrending) from nonstationary, fractal and multifractal time series. We consider real-valued time series relative to measurements of an underlying dynamical system that evolves through time. We assume that such a dynamical process is predictable to a certain degree by means of a class of recurrent networks called Echo State Network (ESN), which are capable to model a generic dynamical process. In order to isolate the superimposed (multi)fractal component of interest, we define a data-driven filter by leveraging on the ESN prediction capability to identify the trend component of a given input time series. Specifically, the (estimated) trend is removed from the original time series and the residual signal is analyzed with the multifractal detrended fluctuation analysis procedure to verify the correctness of the detrending procedure. In order to demonstrate the effectiveness of the proposed technique, we consider several synthetic time series consisting of different types of trends and fractal noise components with known characteristics. We also process a real-world dataset, the sunspot time series, which is well-known for its multifractal features and has recently gained attention in the complex systems field. Results demonstrate the validity and generality of the proposed detrending method based on ESNs.
Year
DOI
Venue
2015
10.1016/j.ins.2016.12.015
Information Sciences
Keywords
Field
DocType
Fractal time series,Multiscaling,Fluctuation analysis,Detrending,Echo state network,Prediction
Complex system,Detrended fluctuation analysis,Artificial intelligence,Dynamical system,Residual,Data-driven,Fractal,Algorithm,Speech recognition,Echo state network,Multifractal system,Machine learning,Mathematics
Journal
Volume
Issue
ISSN
382
C
0020-0255
Citations 
PageRank 
References 
4
0.38
13
Authors
5
Name
Order
Citations
PageRank
Enrico Maiorino1212.91
Filippo Maria Bianchi216015.76
Lorenzo Livi330425.67
Antonello Rizzi436341.68
alireza sadeghian540.38