Open AccessA reservoir computing approach for forecasting and regenerating both dynamical and time-delay controlled financial system behavior
Author(s) -
Rajat Budhiraja,
M. Anand Kumar,
Mrinal K. Das,
Anil Singh Bafila,
Sanjeev Singh
Publication year - 2021
Publication title -
plos one
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 0.99
H-Index - 332
ISSN - 1932-6203
DOI - 10.1371/journal.pone.0246737
Subject(s) - reservoir computing , computer science , echo state network , panacea (medicine) , task (project management) , computation , term (time) , artificial neural network , artificial intelligence , econometrics , industrial engineering , recurrent neural network , economics , engineering , algorithm , medicine , physics , alternative medicine , management , pathology , quantum mechanics
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.