TY - GEN
T1 - A Weighted Fuzzy Time Series Forecasting Model
AU - Ortiz-Arroyo, Daniel
AU - Poulsen, Jens Runi
PY - 2018/7
Y1 - 2018/7
N2 - 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.
AB - 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.
KW - Time Series Analysis
KW - Fuzzy logic
KW - Aggregation Operators
U2 - 10.17485/ijst/2018/v11i27/130708
DO - 10.17485/ijst/2018/v11i27/130708
M3 - Conference article in Journal
SN - 0974-6846
VL - 11
SP - 1
EP - 11
JO - Indian Journal of Science and Technology
JF - Indian Journal of Science and Technology
IS - 27
ER -