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

Published: 31 Oct 2016 | Version 1 | DOI: 10.17632/35fw8pb6s9.1
Contributor(s):

Description of this data

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".

Experiment data files

peer reviewed

This data is associated with the following peer reviewed publication:

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

Published in: Applied Soft Computing

Latest version

  • Version 1

    2016-10-31

    Published: 2016-10-31

    DOI: 10.17632/35fw8pb6s9.1

    Cite this dataset

    Askari Lasaki, Salar (2016), “A clustering based forecasting algorithm for multivariable fuzzy time series using linear combinations of independent variables”, Mendeley Data, v1 http://dx.doi.org/10.17632/35fw8pb6s9.1

Categories

Applied Sciences

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Licence

CC0 1.0 Learn more

The files associated with this dataset are licensed under a Public Domain Dedication licence.

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You can copy, modify, distribute and perform the work, even for commercial purposes, all without asking permission.

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