Fuzzy time series forecasting methods do not require constraints found in conventional approaches. In addition, due to uncertainty that they contain, many time series to be forecasted should be considered as fuzzy time series. Fuzzy time series forecasting models consist of three steps as fuzzification, identification of fuzzy relations and defuzzification. Although most of the time series enco...

this study is focused on developing an integrated optimization-simulation based genetic algorithm model (iosga) to develop the operational policies for a multi-purpose reservoir system. the objective function of the optimization model is considered to be a linear function of reliability (rel), resiliency (res), and vulnerability (vul) of the river-reservoir system. genetic algorithm (ga) is emp...

in a structural time series regression model, binary variables have been used to quantify qualitative or categorical quantitative events such as politic and economic structural breaks, regions, age groups and etc. the use of the binary dummy variables is not reasonable because the effect of an event decreases (increases) gradually over time not at once. the simple and basic idea in this paper i...

this paper presents a fuzzy approach to the prediction of highly nonlinear timeseries.the optimized mamdani-type fuzzy system denoted sqp-flc is applied forthe input-output modeling of measured data. in order to tune fuzzy membershipfunctions, a sequential quadratic programming (sqp) method is employed. theproposed method is evaluated and validated on a highly complex time series, dailygold pri...