Time series forecasting based on echo state network and empirical wavelet transformation

Gao, Ruobin; Du, Liang; Duru, Okan; Yuen, Kum Fai

doi:10.1016/j.asoc.2021.107111

Cited by 56 publications

(17 citation statements)

References 33 publications

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“…Despite being based on a quite simple training framework, RC has shown remarkable performance in various benchmark tasks, such as time series forecasting 23 and image recognition 24 . Moreover, Pathak et al 7,8 demonstrated that ESN approaches are useful for chaotic time series prediction.…”

Section: Discussionmentioning

confidence: 99%

Adapting reservoir computing to solve the Schrödinger equation

Domingo¹,

Borondo²,

Borondo³

2022

Preprint

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Reservoir computing is a machine learning algorithm that excels at predicting the evolution of time series, in particular, dynamical systems. Moreover, it has also shown superb performance at solving partial differential equations. In this work, we adapt this methodology to integrate the time-dependent Schrödinger equation, propagating an initial wavefunction in time. Since such wavefunctions are complex-valued high-dimensional arrays the reservoir computing formalism needs to be extended to cope with complex-valued data. Furthermore, we propose a multi-step learning strategy that avoids overfitting the training data. We illustrate the performance of our adapted reservoir computing method by application to four standard problems in molecular vibrational dynamics.

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Section: Discussionmentioning

confidence: 99%

Adapting reservoir computing to solve the Schrödinger equation

Domingo¹,

Borondo²,

Borondo³

2022

Preprint

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“…Reservoir computing models have gained popularity in various applications, including image classification [30] and time series prediction. [31,32] The echo state network (ESN), [33] a widely used reservoir computing model, differs in function and structure from artificial neural networks and recurrent neural networks. ESNs employ a random sparse matrix to determine their internal connections.…”

Section: The Ds Nanowire Network 31 Deep Echo State Networkmentioning

confidence: 99%

Exploring reservoir computing: Implementation via double stochastic nanowire networks

Tang 唐,

Xia 夏,

Li 李

et al. 2024

Chinese Phys. B

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Neuromorphic computing, inspired by the human brain, uses memristor devices for complex tasks. Recent studies show that self-organizing random nanowires can implement neuromorphic information processing, enabling data analysis. This paper presents a model based on these nanowire networks, with an improved conductance variation profile. We suggest using these networks for temporal information processing via a reservoir computing scheme and propose an efficient data encoding method using voltage pulses. The nanowire network layer generates dynamic behaviors for pulse voltages, allowing time series prediction analysis. Our experiment uses a double stochastic nanowire network architecture (DS-nanowire network) for processing multiple input signals, outperforming traditional reservoir computing in terms of fewer nodes, enriched dynamics, and improved prediction accuracy. Experimental results confirm the high accuracy of this architecture on multiple real-time series datasets, making neuromorphic nanowire networks promising for physical implementation of reservoir computing.

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“…For instance, Haluszczynski et al 27 improve the prediction performance of RC via reducing network size of the reservoir. Gao et al 29 modify RC method by preprocessing the input data via empirical wavelet transforms. Gauthier et al 28 develop a novel RC with less training meta-parameters (called the next generation reservoir computing) by using the nonlinear vector autoregression on the basis of the equivalence between the reservoir computing and the nonlinear vector autoregression.…”

Section: Introductionmentioning

confidence: 99%

Synchronization of non-smooth chaotic systems via an improved reservoir computing

Wu,

Tang,

Liang

2024

Sci Rep

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The reservoir computing (RC) is increasingly used to learn the synchronization behavior of chaotic systems as well as the dynamical behavior of complex systems, but it is scarcely applied in studying synchronization of non-smooth chaotic systems likely due to its complexity leading to the unimpressive effect. Here proposes a simulated annealing-based differential evolution (SADE) algorithm for the optimal parameter selection in the reservoir, and constructs an improved RC model for synchronization, which can work well not only for non-smooth chaotic systems but for smooth ones. Extensive simulations show that the trained RC model with optimal parameters has far longer prediction time than those with empirical and random parameters. More importantly, the well-trained RC system can be well synchronized to its original chaotic system as well as its replicate RC system via one shared signal, whereas the traditional RC system with empirical or random parameters fails for some chaotic systems, particularly for some non-smooth chaotic systems.

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Time series forecasting based on echo state network and empirical wavelet transformation

Cited by 56 publications

References 33 publications

Adapting reservoir computing to solve the Schrödinger equation

Adapting reservoir computing to solve the Schrödinger equation

Exploring reservoir computing: Implementation via double stochastic nanowire networks

Synchronization of non-smooth chaotic systems via an improved reservoir computing

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