Non-Probabilistic Inverse Fuzzy Model in Time Series Forecasting

Many models and techniques have been proposed by researchers to improve forecasting accuracy using fuzzy time series. However, very few studies have tackled problems that involve inverse fuzzy function into fuzzy time series forecasting. In this paper, we modify inverse fuzzy function by considering new factor value in establishing the forecasting model without any probabilistic approaches. The proposed model was evaluated by comparing its performance with inverse and non�inverse fuzzy time series models in forecasting the yearly enrollment data of several universities, such as Alabama University, Universiti Teknologi Malaysia (UTM), and QiongZhou University; the yearly car accidents in Belgium; and the monthly Turkish spot gold price. The results suggest that the proposed model has potential to improve the forecasting accuracy compared to the existing inverse and non-inverse fuzzy time series models. This paper contributes to providing the better future forecast values using the systematic rules. Keywords: Fuzzy time series, inverse fuzzy function, non-probabilistic model, non-inverse fuzzy model, future forecas

Fish swarmed Fuzzy Time Series for Photovoltaic’s Forecasting in Microgrid

Forecasting irradiation and temperature is important for designing photovoltaic systems because these two factors have a significant impact on system performance. Irradiation refers to the amount of solar radiation that reaches the earth's surface, and directly affects the amount of energy that can be generated by a photovoltaic system. Therefore, accurate irradiation forecasting is essential for estimating the amount of energy a photovoltaic system can produce, and can assist in determining the appropriate system size, configuration, and orientation to maximize energy output. Temperature also plays an important role in the performance of a photovoltaic system. With increasing temperature, the efficiency of the solar cell decreases, which means that the energy output of the system also decreases. Therefore, accurate temperature forecasts are essential for estimating system energy output, selecting suitable materials, and designing effective cooling systems to prevent overheating. In summary, forecasting irradiation and temperature is important for designing photovoltaic systems as it helps in determining suitable system size, configuration, orientation, material selection, and cooling system, which ultimately results in higher energy output and better system performance. In recent decades, many forecasting models have been built on the idea of fuzzy time series. There are several forecasting models proposed by integrating fuzzy time series with heuristic or evolutionary algorithms such as genetic algorithms, but the results are not satisfactory. To improve forecasting accuracy, a new hybrid forecasting model combines fish swarm optimization algorithm with fuzzy time series. The results of irradiance prediction/forecasting with the smallest error are using the type of Fuzzy Time Series prediction model optimized with FSOA with RMSE is 0.83832

Data driven analysis using fuzzy time series for air quality management in Surabaya

One of the environmental issues that can affect human health is air pollution. As the second largest city in Indonesia, economic development and infrastructure construction in the city of Surabaya led to the increasing role of industrial and motor vehicle use which is proportional to the increase in fuel oil consumption. This condition ultimately led to declining air quality. Gas pollutants that contribute to air pollution such as CO, SO2, O3, NO2 and particulate matter PM10 are pollutants that have a direct impact on health. This study aims to analyze, monitor and predict air pollutant concentrations recorded by the Environment Agency Surabaya City based on time series with Fuzzy Time Series.MAPE calculation results on the parameters of pollutants are NO2: 23.6%, CO: 19.5%, O3: 22.75%, PM10: 9.96% and SO2: 3.6%

Fuzzy time series forecasting method based on Gustafson-Kessel fuzzy clustering

Fuzzy time series approaches have being increasingly attracted researchers' attentions. The procedures on fuzzy time series actually consist of three stages; fuzzification, determination of fuzzy relations and defuzzification. Researches are generally concentrated on these stages and about improving them. In this study, we propose a new approach, which combines several techniques. In this approach, Gustafson-Kessel, which is a fuzzy clustering technique, is being used to fuzzification of time series. The proposed method is compared with the approaches in literature