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A Weighted Fuzzy Time Series Forecasting Model


Affiliations
1 Department of Energy Technology, Aalborg University Esbjerg, Denmark
 

In this paper we describe a new automatic partitioning method and a first order weighted fuzzy time series forecasting model. First, we show that our automatic fuzzy partitioning method provides an accurate approximation to the original time series. The fuzzy sets extracted from our partitioning are grouped to create a rule-base that will be used in forecasting. We found that the accuracy of our first order model is improved when an ordered weighting averaging operator is applied. The model presented in this paper does not attempt to produce the most accurate forecasting results, when compared with other more complex higher order models. Our goal is to show that there is still space for improvement when simple first order forecasting models are used. Our results show that the combination of a simple partitioning method, a first order model, and an averaging operator is still capable of outperforming not only the first order models that have proposed in the literature but also other higher order models.
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  • A Weighted Fuzzy Time Series Forecasting Model

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Authors

Daniel Ortiz-Arroyo
Department of Energy Technology, Aalborg University Esbjerg, Denmark
Jens Runi Poulsen
Department of Energy Technology, Aalborg University Esbjerg, Denmark

Abstract


In this paper we describe a new automatic partitioning method and a first order weighted fuzzy time series forecasting model. First, we show that our automatic fuzzy partitioning method provides an accurate approximation to the original time series. The fuzzy sets extracted from our partitioning are grouped to create a rule-base that will be used in forecasting. We found that the accuracy of our first order model is improved when an ordered weighting averaging operator is applied. The model presented in this paper does not attempt to produce the most accurate forecasting results, when compared with other more complex higher order models. Our goal is to show that there is still space for improvement when simple first order forecasting models are used. Our results show that the combination of a simple partitioning method, a first order model, and an averaging operator is still capable of outperforming not only the first order models that have proposed in the literature but also other higher order models.

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DOI: https://doi.org/10.17485/ijst%2F2018%2Fv11i27%2F130708