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The fuzzy time series is introduced by Song and Chissom to construct a pattern for time series with vague or linguistic value. Many methods using the interval and fuzzy logical relationship related with historical data have been suggested to enhance the forecasting accuracy. But they do not fully reflect the fluctuation of historical data. Therefore, we propose the interval rearranged method to reflect...
This paper represents a fusion model of functional link artificial neural network (FLANN) based on Kernel Regression (KR) for modeling and prediction of exchange rate time series. To predict the exchange rate, we process the exchange rate datasets with KR to smooth the noise. And then the smoothed datasets are nonlinearly expanded using the sine and cosine expansions before inputting to the FLANN...
Semi-parametric regression model prediction method based on empirical mode decomposition was studied in this paper. Firstly, basic idea of the empirical mode decomposition was introduced, and the improved algorithm was proposed to mitigate the end effect in the iterative shift process. Secondly, least squares method was employed to estimate the parameter β based on the trend component of empirical...
The majorities of the existing predictors for states are model-dependent and therefore require some prior knowledge for the identification of complex systems, usually involving system identification, extensive training or online adaptation in the case of time-varying systems. In this paper a model-free predictor (MFP) for a time series produced by an unknown nonlinear system or process is proposed...
The rainfall intensity of annual typhoon is high in Taiwan. It often result in the downstream flooding and losses of economics. Thus, simulation model of rainfall-runoff is an very important subject . In past research, time series model often forecast water level . But time series often have an influence on one time lag for forecasting of data.When reach to peak water level ,the error occurs in the...
This paper applies TSK fuzzy inference system to the GARCH model for predicting the conditional volatility of foreign exchange rates returns. Out-of-sample forecast results of using TSK-based GARCH model are compared with that of an ANN-based and a SVM-based GARCH models, respectively. The empirical study shows that for the RMSE, MAE and Mincer-Zarnowitz regression test, the TSK-based GARCH model...
Aiming at the shortage of traditional seasonal variation model, the combination of gray model with self-memory model was proposed for improving traditional seasonal variation model. The application of gray model used to dig out the long time trend and self-memory model to solve problem of non-linearity of the model's seasonal fluctuation. The case study showed that the proposed model has higher precision...
Over the past few years, a considerable number of studies have been proposed on load forecasting. This paper aims at proposing a promising model using high-order adaptive fuzzy time-series algorithm to get more efficient forecasting. From the reviewed literature related to fuzzy time-series, there are two points need to be concerned. The first is to determine a reasonable universe of discourse and...
The purpose of this study is to gain insights into the residual value of selected groups of heavy construction equipment and to develop a mathematical model for its prediction.
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