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A clustering based forecasting algorithm for multivariable fuzzy time series using linear combinations of independent variables

By S Askari Lasaki

Abstract

Dear Researcher, Thank you for using this code and datasets. I explain how CFTS code related to my paper "A clustering based forecasting algorithm for multivariable fuzzy time series using linear combinations of independent variables" published in Applied Soft Computing works. All datasets mentioned in the paper accompanied with CFTS code are included. If there is any question feel free to contact me at: bas_salaraskari@yahoo.com s_askari@aut.ac.ir Regards, S. Askari Guidelines for CFTS algorithm: 1. Open the file CFTS Code using MATLAB. 2. Enter or paste name of the dataset you wish to simulate in line 5 after "load". It loads the dataset in the workplace. 3. Lines 6 and 7: "r" is number of independent variables and "N" is number of data vectors used for training. 4. Line 9: "C" is number of clusters. You can use the optimal number of clusters given in Table 6 of paper or your own preferred value. 5. If line 28 is "comment", covariance norm (Mahalanobis distance) is use and if it is "uncomment", identity norm (Euclidean distance) is used. 6. Please press Ctrl Enter to run the code. 7. For your own dataset, please arrange the data as the datasets described in MS Word file "Read Me"

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