Abstract
Our work on regression and classification provides a new contribution to the analysis of time series used in many areas for years. Owing to the fact that convergence could not obtained with the methods used in autocorrelation fixing process faced with time series regression application, success is not met or fall into obligation of changing the models’ degree. Changing the models’ degree may not be desirable in every situation. In our study, recommended for these situations, time series data was fuzzified by using the simple membership function and fuzzy rule generation technique (SMRGT) and to estimate future an equation has created by applying fuzzy least square regression (FLSR) method which is a simple linear regression method to this data. Although SMRGT has success in determining the flow discharge in open channels and can be used confidently for flow discharge modeling in open canals, as well as in pipe flow with some modifications, there is no clue about that this technique is successful in fuzzy linear regression modeling. Therefore, in order to address the luck of such a modeling, a new hybrid model has been described within this study. In conclusion, to demonstrate our methods’ efficiency, classical linear regression for time series data and linear regression for fuzzy time series data were applied to two different data sets, and these two approaches performances were compared by using different measures.
Highlights
While the modeling of some systems that human estimation is effective, it can be encountered with a fuzzy structure
At the end of the Prais-Winsten procedure, fixes autocorrelation gradually, autocorrelation could not removed or fall into obligation of changing the models’ degree. In these undesirable situations a new hybrid model is proposed based on the basic concepts of SMRGT to fuzzify variables and fuzzy regression models approaches to time-series forecasting
The performance measures that we used in our applications are Adjusted R2 (Adj-R2), R2, Mean Absolute Error (MAE), Mean Absolute Percentage Error (MAPE), Mean Square Error (MSE), Root Mean Square Error (RMSE), Akaike’s Information Criteria (AIC) and Correlation Coefficient
Summary
While the modeling of some systems that human estimation is effective, it can be encountered with a fuzzy structure These fuzzy structure parameters can be presented as a fuzzy linear function obtained from fuzzy sets. Singh proposed a new method of fuzzy time series forecasting based on difference parameters for the accuracy in the forecasted values [7]. All these studies are related directly with fuzzy time series. In order to highlight its appropriateness and effectiveness, our proposed method is applied to two different data sets and their performance is compared with linear regression model for time series
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