Complex network approaches to nonlinear time series analysis
In the last decade, there has been a growing body of literature addressing the utilization of
complex network methods for the characterization of dynamical systems based on time …
complex network methods for the characterization of dynamical systems based on time …
Multifractal analysis of financial markets: A review
Multifractality is ubiquitously observed in complex natural and socioeconomic systems.
Multifractal analysis provides powerful tools to understand the complex nonlinear nature of …
Multifractal analysis provides powerful tools to understand the complex nonlinear nature of …
[HTML][HTML] Battery lifetime prognostics
Lithium-ion batteries have been widely used in many important applications. However, there
are still many challenges facing lithium-ion batteries, one of them being degradation. Battery …
are still many challenges facing lithium-ion batteries, one of them being degradation. Battery …
Remaining useful life prediction of lithium-ion batteries based on false nearest neighbors and a hybrid neural network
Accurate estimation of the remaining useful life of lithium-ion batteries is critically important
for electronic devices. In the existing literature, the widely applied model-based approaches …
for electronic devices. In the existing literature, the widely applied model-based approaches …
Robustness of LSTM neural networks for multi-step forecasting of chaotic time series
M Sangiorgio, F Dercole - Chaos, Solitons & Fractals, 2020 - Elsevier
Recurrent neurons (and in particular LSTM cells) demonstrated to be efficient when used as
basic blocks to build sequence to sequence architectures, which represent the state-of-the …
basic blocks to build sequence to sequence architectures, which represent the state-of-the …
[HTML][HTML] Evaluating time series forecasting models: An empirical study on performance estimation methods
Performance estimation aims at estimating the loss that a predictive model will incur on
unseen data. This process is a fundamental stage in any machine learning project. In this …
unseen data. This process is a fundamental stage in any machine learning project. In this …
Energy-fluctuated multiscale feature learning with deep convnet for intelligent spindle bearing fault diagnosis
X Ding, Q He - IEEE Transactions on Instrumentation and …, 2017 - ieeexplore.ieee.org
Considering various health conditions under varying operational conditions, the mining
sensitive feature from the measured signals is still a great challenge for intelligent fault …
sensitive feature from the measured signals is still a great challenge for intelligent fault …
[HTML][HTML] Surrogate data for hypothesis testing of physical systems
G Lancaster, D Iatsenko, A Pidde, V Ticcinelli… - Physics Reports, 2018 - Elsevier
The availability of time series of the evolution of the properties of physical systems is
increasing, stimulating the development of many novel methods for the extraction of …
increasing, stimulating the development of many novel methods for the extraction of …
A novel bearing fault diagnosis model integrated permutation entropy, ensemble empirical mode decomposition and optimized SVM
X Zhang, Y Liang, J Zhou - Measurement, 2015 - Elsevier
This paper presents a novel hybrid model for fault detection and classification of motor
bearing. In the proposed model, permutation entropy (PE) of the vibration signal is …
bearing. In the proposed model, permutation entropy (PE) of the vibration signal is …
Wind speed forecasting for wind farms: A method based on support vector regression
G Santamaría-Bonfil, A Reyes-Ballesteros… - Renewable Energy, 2016 - Elsevier
In this paper, a hybrid methodology based on Support Vector Regression for wind speed
forecasting is proposed. Using the autoregressive model called Time Delay Coordinates …
forecasting is proposed. Using the autoregressive model called Time Delay Coordinates …