Abstract
Fuzzy Cognitive Maps (FCMs) have been widely employed as nonlinear forecasting methods that are easily interpretable. They have a remarkable capability to enhance accuracy and are well-equipped to handle uncertainty and emulate the dynamics of complex systems. The main goal of this article is to present a new randomized multiple-input multiple-output (MIMO) FCM-based forecasting technique named M-PRFCM to forecast real-world high-dimensional time series in Internet of Things (IoT) applications. M-PRFCM is a first-order forecasting method integrating the concepts of randomized FCMs, Echo State Networks (ESNs), and Kernel Principle Components Analysis (KPCA). The training process of M-PRFCM is accelerated as a result of utilizing the ESN weight initialization trick, which randomly selects weights. The obtained results show the efficacy and validation of the proposed technique in terms of accuracy when compared with other existing approaches.
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Acknowledgments
This work has been supported by the Brazilian agencies (i) National Council for Scientific and Technological Development (CNPq), Grants no. 306850/2016-8 and 312991/2020-7; (ii) Coordination for the Improvement of Higher Education Personnel - Brazil (CAPES) through the Academic Excellence Program (PROEX) and (iii) Foundation for Research of the State of Minas Gerais (FAPEMIG, in Portuguese), Grant no. APQ-01779-21.
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Orang, O., Bitencourt, H.V., Silva, P.C.d.L.e., Guimarães, F.G. (2024). Time Series Forecasting Using Parallel Randomized Fuzzy Cognitive Maps and Reservoir Computing. In: García Márquez, F.P., Jamil, A., Hameed, A.A., Segovia Ramírez, I. (eds) Emerging Trends and Applications in Artificial Intelligence. ICETAI 2023. Lecture Notes in Networks and Systems, vol 960. Springer, Cham. https://doi.org/10.1007/978-3-031-56728-5_5
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