Time series prediction using random weights fuzzy neural networks

In this paper, we introduce Random Weights Fuzzy Neural Networks as a suitable tool for solving prediction problems. The generalization capability of these randomized fuzzy neural networks is exploited in order to estimate accurately the sample be predicted from a multidimensional input. The latter is obtained by applying an embedding technique to the time series, which selects only the meaningful past samples to be used for prediction. We tested the proposed approach on real-world time series pertaining to the application context of power delivery. We proved the efficacy of the proposed approach by comparing its forecasting accuracy with respect to other prediction systems based on well-known data-driven regression models.