Codes and Datasets for (A clustering based forecasting algorithm for multivariable fuzzy time series using linear combinations of independent variables)

Published: 9 Oct 2016 | Version 2 | DOI: 10.17632/7z6fdhkxwz.2
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 together 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

Experiment data files

Steps to reproduce

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. For your own dataset, please arrange the data as the datasets given here. For details. please read the "Read Me" included here.

This data is associated with the following 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 2

    2016-10-09

    Published: 2016-10-09

    DOI: 10.17632/7z6fdhkxwz.2

    Cite this dataset

    Askari Lasaki, Salar (2016), “Codes and Datasets for (A clustering based forecasting algorithm for multivariable fuzzy time series using linear combinations of independent variables)”, Mendeley Data, v2 http://dx.doi.org/10.17632/7z6fdhkxwz.2

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Institutions

Amirkabir University of Technology

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.

What does this mean?

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

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