Institute of Informatics and Communication, University of Delhi South Campus, New Delhi, India.
Department of Computer Science Engineering, School of Engineering and Technology, Central University of Haryana, Mahendergarh, Haryana, India.
PLoS One. 2021 Feb 12;16(2):e0246737. doi: 10.1371/journal.pone.0246737. eCollection 2021.
Significant research in reservoir computing over the past two decades has revived interest in recurrent neural networks. Owing to its ingrained capability of performing high-speed and low-cost computations this has become a panacea for multi-variate complex systems having non-linearity within their relationships. Modelling economic and financial trends has always been a challenging task owing to their volatile nature and no linear dependence on associated influencers. Prior studies aimed at effectively forecasting such financial systems, but, always left a visible room for optimization in terms of cost, speed and modelling complexities. Our work employs a reservoir computing approach complying to echo-state network principles, along with varying strengths of time-delayed feedback to model a complex financial system. The derived model is demonstrated to act robustly towards influence of trends and other fluctuating parameters by effectively forecasting long-term system behavior. Moreover, it also re-generates the financial system unknowns with a high degree of accuracy when only limited future data is available, thereby, becoming a reliable feeder for any long-term decision making or policy formulations.
在过去的二十年中，对储层计算的大量研究重新激发了人们对递归神经网络的兴趣。由于其固有的高速低成本计算能力，它已成为具有非线性关系的多变量复杂系统的万能药。由于其不稳定的性质以及与相关影响因素没有线性关系，因此对经济和金融趋势进行建模一直是一项具有挑战性的任务。以前的研究旨在有效地预测这些金融系统，但是，在成本，速度和建模复杂性方面，始终存在明显的优化空间。我们的工作采用了一种符合回声状态网络原则的储层计算方法，并结合了时滞反馈的不同强度来对复杂的金融系统进行建模。所得到的模型被证明能够通过有效地预测长期系统行为，对趋势和其他波动参数的影响表现出稳健性。此外，当只有有限的未来数据可用时，它还可以高度准确地生成金融系统的未知内容，从而成为任何长期决策或政策制定的可靠依据。
