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Multi-output time series forecasting with randomized multivariate Fuzzy Cognitive Maps.
- Source :
-
Chaos, Solitons & Fractals . Nov2023, Vol. 176, pN.PAG-N.PAG. 1p. - Publication Year :
- 2023
-
Abstract
- Fuzzy Cognitive Maps (FCMs) have become a relevant technique for modeling and forecasting time series due to their advantages in dealing with uncertainty and simulating the dynamics of complex systems. Although numerous univariate and multivariate FCM-based forecasting models have been presented in the literature, one of the still open questions is how to enable FCMs to forecast multivariate time series for multiple-input, multiple-output (MIMO) systems with an efficient learning mechanism. from a computational point of view. This paper suggests a randomized MIMO FCM-based forecasting approach called MO-RHFCM to predict low-dimensional multivariate time series. More specifically, MO-RHFCM is a hybrid model merging the concepts of multivariate fuzzy time series, high order FCM (HFCM), and Echo State Networks (ESN). The structure of MO-RHFCM consists of three layers: input layer, reservoir (internal) layer, and output layer. Only the output layer is trainable using the Least Squares minimization algorithm; hence training the proposed MO-RHFCM method is fast and simple. The weights inside each sub-reservoir are selected randomly and remain fixed during the training process. The obtained results indicate the efficacy and validity of the proposed MO-RHFCM technique compared with some machine learning and deep learning baseline models. • Introducing a new randomized multivariate FCM model as a reservoir computing. • The first introduction of FCM-based forecasting method for multiple outputs systems. • The superior performance of the proposed model in comparison to other baselines. • Less complexity of our proposed model compared to baseline deep learning models. [ABSTRACT FROM AUTHOR]
Details
- Language :
- English
- ISSN :
- 09600779
- Volume :
- 176
- Database :
- Academic Search Index
- Journal :
- Chaos, Solitons & Fractals
- Publication Type :
- Periodical
- Accession number :
- 173324080
- Full Text :
- https://doi.org/10.1016/j.chaos.2023.114077