Abstract In this paper we propose a framework for fuzzy clustering of time series based on directional volatility spillovers. the case financial series, detecting clusters spillovers provides insights into market structure, which can be useful to both portfolio managers and policy makers. We measure directional—i.e. “From” “To” others—volatility with methodology generalized forecast-error varia...

This study proposes a new time series prediction method that combines Fuzzy Time Series (FTS) based on fuzzy clustering and Maximal Overlap Discrete Wavelet Transform (MODWT). generally consist of subseries, each which reflects the different behavior using single for all subseries can be negatively impacted forecasting accuracy. Proposed is decomposing into sub-time through MODWT predicting an ...

A self-organized fuzzy neural network (SOFNN) with a reinforcement learning algorithm called Stochastic Gradient Ascent (SGA) is proposed to forecast a set of 11 time series. The proposed system is confirmed to predict chaotic time series before, and is applied to predict each/every time series in NN3 forecasting competition modifying parameters of threshold of fuzzy neurons only. The training ...

Several time-variant fuzzy time series models have been developed during the last decade. These models usually focus on forecasting stationary of trend time series, but they are not suitable for forecasting seasonal time series. Furthermore, several factors that affect the forecasting accuracy are not carefully examined, such as interval length, interval number, and level of window base. Aiming...

In this paper, a novel fuzzy rule transfer mechanism for self-constructing neural fuzzy inference networks is being proposed. The features of the proposed method, termed data-driven neural fuzzy system with collaborative fuzzy clustering mechanism (DDNFS-CFCM) are; (1) Fuzzy rules are generated facilely by fuzzy c-means (FCM) and then adapted by the preprocessed collaborative fuzzy clustering (...