<abstract xmlns="http://www.w3.org/1999/xhtml"><p>The goal of this paper is to propose a new method for fuzzy forecasting of time series with supervised learning and k-order fuzzy relationships. In the training phase based on k previous historical periods, a multidimensional matrix of fuzzy dependencies is constructed. During the test stage, the fitted fuzzy model is run for validating the observations and each output value is predicted by using a fuzzy input vector of k previous intervals. The proposed algorithm is verified by a benchmark dataset for fuzzy time series forecasting. The results obtained are similar or better than those of other fuzzy time series prediction methods. Comparative analysis shows the high potential of the new algorithm as an alternative to fuzzy prediction and reveals some opportunities for its further improvement.</p></abstract>

The goal of this paper is to propose a new method for fuzzy forecasting of time series with supervised learning and k-order fuzzy relationships. In the training phase based on k previous historical periods, a multidimensional matrix of fuzzy dependencies is constructed. During the test stage, the fitted fuzzy model is run for validating the observations and each output value is predicted by using a fuzzy input vector of k previous intervals. The proposed algorithm is verified by a benchmark dataset for fuzzy time series forecasting. The results obtained are similar or better than those of other fuzzy time series prediction methods. Comparative analysis shows the high potential of the new algorithm as an alternative to fuzzy prediction and reveals some opportunities for its further improvement.

Fuzzy Supervised Multi-Period Time Series Forecasting

Paisii Hilendarski, University of Plovdiv

Cybernetics and Information Technologies

This work is licensed under the Creative Commons Attribution-NonCommercial-NoDerivatives 3.0 License.

{"article-title":"Fuzzy Supervised Multi-Period Time Series Forecasting"}

The goal of this paper is to propose a new method for fuzzy forecasting of time series with supervised learning and k-order fuzzy relationships. In the...

Fuzzy Supervised Multi-Period Time Series Forecasting

Published Online: Jun 18, 2019

Page range: 74 - 86

Received: Dec 10, 2018

Accepted: Apr 18, 2019

DOI: https://doi.org/10.2478/cait-2019-0016

Keywords
Fuzzy set, fuzzy time series, forecasting, membership function, fuzzy relationships

© 2019 Galina Ilieva, published by Sciendo

This work is licensed under the Creative Commons Attribution-NonCommercial-NoDerivatives 3.0 License.

Fuzzy Supervised Multi-Period Time Series Forecasting

Published Online: Jun 18, 2019

Page range: 74 - 86

Received: Dec 10, 2018

Accepted: Apr 18, 2019

DOI: https://doi.org/10.2478/cait-2019-0016

KeywordsFuzzy set, fuzzy time series, forecasting, membership function, fuzzy relationships

© 2019 Galina Ilieva, published by Sciendo

This work is licensed under the Creative Commons Attribution-NonCommercial-NoDerivatives 3.0 License.

Keywords
Fuzzy set, fuzzy time series, forecasting, membership function, fuzzy relationships