Fuzzy Cognitive Maps (FCMs) have emerged as an interpretable signed weighted digraph method consisting of nodes (concepts) and weights which represent the dependencies among the concepts. Although FCMs have attained considerable achievements in various time series prediction applications, designing an FCM model with time-efficient training method is still an open challenge. Thus, this paper introduces a novel univariate time series forecasting technique, which is composed of a group of randomized high order FCM models labeled R-HFCM. The novelty of the proposed R-HFCM model is relevant to merging the concepts of FCM and Echo State Network (ESN) as an efficient and particular family of Reservoir Computing (RC) models, where the least squares algorithm is applied to train the model. From another perspective, the structure of R-HFCM consists of the input layer, reservoir layer, and output layer in which only the output layer is trainable while the weights of each sub-reservoir components are selected randomly and keep constant during the training process. As case studies, this model considers solar energy forecasting with public data for Brazilian solar stations as well as Malaysia dataset, which includes hourly electric load and temperature data of the power supply company of the city of Johor in Malaysia. The experiment also includes the effect of the map size, activation function, the presence of bias and the size of the reservoir on the accuracy of R-HFCM method. The obtained results confirm the outperformance of the proposed R-HFCM model in comparison to the other methods. This study provides evidence that FCM can be a new way to implement a reservoir of dynamics in time series modelling.
翻译:· 本文件引入了一种新的单一时间序列预测技术,该技术由一组随机高排序的FCM模型组成,标记为R-HFCM。拟议的RHCM模型的新颖性与将FCM和回声国家网络的概念合并为 " Reservoir 计算器(RC)模型(REC) " 的有效和特定系列模型有关,在各种时间序列预测应用中取得了相当大的成就,设计了一个具有时间效率培训方法的FCM模型模型,这仍然是一个公开的挑战。因此,本文件引入了一个全新的单向时间序列预测技术,该技术由一组随机高排序的FCMM模型组成,标记为R-HFCM;拟议的RHCM模型的新颖性,将FCM和回声国家网络(ESN)的概念合并为 " Reservoorvoor " (ESN) ) 和 " ESCMN " 动态(ESN)的概念,作为 " Reservooral " 计算器 " 组合 " 模型 " 模型 " 的高效和 " Excountal " 计算 " 模型 " 模型 " 模型 " 模型 " 模型 " 模型 " RSRMylational " RSRMyal " 数据,在模型中可以将RFCMMMMY " Rex " Rex " Rex " Rex " RF " RFMMMMM " 的模型中提供量数据作为模型,在马来西亚的 " 的 " 的 " 的模型中提供量数据作为实验性数据,作为模型的模型的模型的模型的模型,作为数据,作为模型,在城市的模型的模型的模型中的数据,将这一模型中的数据,包括了其他数据,作为模型,作为模型的模型的模型的模型的模型的模型的模型的模型的模型,作为模型,作为模型的模型的模型的模型的模型的模型的模型的模型,用于的模型,用于基值。的模型,用于的模型,用于的模型,用于的模型的模型,用于数据的运行的运行的运行的运行的运行的运行的运行的运行的运行的运行的运行的运行的运行的运行的运行的运行的运行的运行的运行的运行的运行的运行的运行的运行的运行的运行的