<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

Online veröffentlicht: 18. Juni 2019

Seitenbereich: 74 - 86

Eingereicht: 10. Dez. 2018

Akzeptiert: 18. Apr. 2019

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

Schlüsselwörter
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

Online veröffentlicht: 18. Juni 2019

Seitenbereich: 74 - 86

Eingereicht: 10. Dez. 2018

Akzeptiert: 18. Apr. 2019

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

SchlüsselwörterFuzzy 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.

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