Meteorological forecasting is an important issue in research. Typically, the forecasting is performed at “global level,” by gathering data in a large geographical region and by studying their evolution, thus foreseeing the meteorological situation in a certain place. In this paper a “local level” approach, based on time series forecasting using Type-2 Fuzzy Systems, is proposed. In particular t...

The least square support vector machines (LSSSVM) model is a novel forecasting approach and has been successfully used to solve time series problems. However, the applications of LSSVM model in a seasonal time series forecasting has not been widely investigated. This study aims at developing a LSSVM model to forecast seasonal time series data. To assess the effectiveness of this model, the airl...

The decomposition of time series into components is an important task that helps to understand and can enable better forecasting. Nowadays, with high sampling rates leading high-frequency data (such as daily, hourly, or minutely data), many real-world datasets contain that can exhibit multiple seasonal patterns. Although several methods have been proposed to decompose under these circumstances,...

Based on the works [11], [22] a fuzzy time series model is proposed and applied to predict chaotic financial process. The general methodological framework of classical and fuzzy modelling of economic time series is considered. A complete fuzzy time series modelling approach is proposed. To generate fuzzy rules from data, the neural network with Supervised Competitive Learning (SCL)-based produc...

The accuracy of short-term wind speed prediction is very important for wind power generation. In this paper, a hybrid method combining ensemble empirical mode decomposition (EEMD), adaptive neural network based fuzzy inference system (ANFIS) and seasonal auto-regression integrated moving average (SARIMA) is presented for short-term wind speed forecasting. The original wind speed series is decom...