Long-term intuitionistic fuzzy time series forecasting model based on DTW

Xiao-shi FAN; Ying-jie LEI; Yan-li LU; Ya-nan WANG

doi:10.11959/j.issn.1000-436x.2016160

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Long-term intuitionistic fuzzy time series forecasting model based on DTW

Papers | 更新时间：2024-10-11

- Long-term intuitionistic fuzzy time series forecasting model based on DTW
- Journal on Communications Vol. 37, Issue 8, Pages: 95-104(2016)
- 作者机构：
  
  空军工程大学防空反导学院，陕西西安 710051
- 作者简介：
- 基金信息：
- DOI：10.11959/j.issn.1000-436x.2016160
  CLC： TP393.08
- Online First：2016-08，
  
  Published：25 August 2016
- 稿件说明：
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Xiao-shi FAN, Ying-jie LEI, Yan-li LU, et al. Long-term intuitionistic fuzzy time series forecasting model based on DTW[J]. Journal on Communications, 2016, 37(8): 95-104.
DOI：

Xiao-shi FAN, Ying-jie LEI, Yan-li LU, et al. Long-term intuitionistic fuzzy time series forecasting model based on DTW[J]. Journal on Communications, 2016, 37(8): 95-104. DOI： 10.11959/j.issn.1000-436x.2016160.

摘要

针对现有直觉模糊时间序列模型中直觉模糊关系组和确定性转换规则过度依赖训练数据规模的问题，提出一种基于动态时间弯曲（DTW

dynamic time warping）距离的长期直觉模糊时间序列预测模型。通过直觉模糊C均值（IFCM

intuitionistic fuzzy C mean）聚类构建直觉模糊时间序列片段库，动态更新和维护规则库，减少系统复杂度。提出基于DTW距离的直觉模糊时间序列片段相似度计算方法，有效解决不等长时间序列片段匹配问题。通过对合成数据以及包含不同时间序列模式的气温数据的实验，与其他相关模型比较，说明该模型对于不同时间序列趋势变化模式中均具有较高的预测能力，克服传统模型提高模型只能满足单一模式时间序列预测，提高模型的泛化性能。

Abstract

In existing fuzzy time series forecasting models

the intuitionistic fuzzy relationship groups and deterministic transition rules excessively relied on scale of the training data.A long-term intuitionistic fuzzy time series (IFTS) fore-casting model based on DTW was proposed.The IFTS segment base was constructed by IFCM.The complexity of sys-tem was reduced by dynamic update and maintaining of the rule base.The computing method of IFTS segments similar-ity based on the distance of DTW was proposed

which was valid for matching unequal length time series segments.The proposed model implements on the synthetic and the temperature dataset

which including different time series patterns

respectively.The experiments illustrate that the forecasting accuracy of the proposed model is higher than the others on the different tendency patterns of time series.The proposed model overcomes the limitation of single time series pattern and improves the generalization ability.

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Keywords

references

SONG Q , CHISSOM C S . Forecasting enrollments with fuzzy time series—part I [J ] . Fuzzy Sets Systems , 1993 , 54 ( 1 ): 1 - 9 .

HUANG Y L , HORNG S J , HE M X , et al . A hybrid forecasting model for enrollments based on aggregated fuzzy time series and particle swarm optimization [J ] . Expert Systems with Applications , 2011 , 38 ( 7 ): 8014 - 8023 .

WANG L Z , LIU X D , PEDRYCZ W . Effective intervals determined by information granules to improve forecasting in fuzzy time series [J ] . Expert Systems with Applications , 2013 , 40 ( 14 ): 1465 - 1470 .

WEI L , CHEN X Y , PEDRYCZ W , et al . Using interval information granules to improve forecasting in fuzzy time series [J ] . International Journal of Approximate Reasoning , 2015 , 57 ( 11 ): 1 - 18 .

CAI Q S , ZHANG D F , ZHANG W . A new fuzzy time series forecast-ing model combined with ant colony optimization and auto-regression [J ] . Knowledge-Based Systems , 2015 , 74 ( 11 ): 61 - 68 .

EGRIOGLU E , ALADAG C H , YOLCU U . Fuzzy time series fore-casting with a novel hybrid approach combining fuzzy C-means and neural networks [J ] . Expert Systems with Applications , 2013 , 40 ( 3 ): 854 - 857 .

PARK J , LEE D J , SONG C K , et al . TAIFEX and KOSPI 200 fore-casting based on two-factor high-order fuzzy time series and particle swarm optimization [J ] . Expert Systems with Applications , 2010 , 37 ( 2 ): 959 - 967 .

CHEN S M , CHEN S W . Fuzzy forecasting based on two-factors second-order fuzzy-trend logical relationship groups and the probabili-ties of trends of fuzzy logical relationships [J ] . IEEE Transactions on cybernetics , 2015 , 45 ( 3 ): 405 - 416 .

ASKARI S , MONTAZERIN N . A high-order multi-variable fuzzy time series forecasting algorithm based on fuzzy clustering [J ] . Expert Systems with Applications , 2015 , 42 ( 9 ): 2121 - 2135 .

CASTILLO O , ALANIS A , GARCIA M , et al . An intuitionistic fuzzy system for time series analysis in plant monitoring and diagnosis [J ] . Applied Soft Computing , 2007 , 7 ( 4 ): 1227 - 1233 .

郑寇全，雷英杰，王睿 , 等 . 直觉模糊时间序列建模及应用 [J ] . 控制与决策， 2013 ， 28 ( 10 ): 1525 - 1530 .

ZHENG K Q , LEI Y J , WANG R , et al . Modeling and application of IFTS [J ] . Control and Decision , 2013 , 28 ( 10 ): 1525 - 1530 .

GANGWAR S S , KUMAR S . Probabilistic and intuitionistic fuzzy sets-based method for fuzzy time series forecasting [J ] . Cybernetics and Systems , 2014 , 45 ( 4 ): 349 - 361 .

郑寇全，雷英杰，王睿 , 等 . 参数自适应的长期 IFTS 预测算法 [J ] . 系统工程与电子技术， 2014 ， 36 ( 1 ): 100 - 104 .

ZHENG K Q , LEI Y J , WANG R , et al . Method of long-term IFTS forecasting based on parameter adaptation [J ] . Systems Engineering and Electronics , 2014 , 36 ( 1 ): 100 - 104 .

郑寇全，雷英杰，王睿 , 等 . 基于确定性转换的IFTS预测 [J ] . 应用科学学报， 2013 ， 31 ( 2 ): 204 - 211 .

ZHENG K Q , LEI Y J , WANG R , et al . Prediction of IFTS based on deterministic transition [J ] . Journal of Applied Sciences , 2013 , 31 ( 2 ): 204 - 211 .

郑寇全，雷英杰，王睿 , 等 . 基于矢量量化的长期直觉模糊时间序列预测 [J ] . 吉林大学学报(工学版) ， 2014 ， 44 ( 3 ): 795 - 800 .

ZHENG K Q , LEI Y J , WANG R , et al . Long-term intuitionistic fuzzy time series forecasting based on vector quantization [J ] . Journal of Jilin University (Engineering and Technology Edition) , 2014 , 44 ( 3 ): 795 - 800 .

LI S T , KUO S C , CHEN Y C , et al . Deterministic vector long-term forecasting for fuzzy time series [J ] . Fuzzy Sets and Systems , 2010 , 161 ( 13 ): 1852 - 1870 .

HUNG K C , LIN K P . Long-term business cycle forecasting through a potential intuitionistic fuzzy least-squares support vector regression approach [J ] . Information Sciences , 2013 , 224 ( Complete ): 37 - 48 .

LEE H S , CHOU M T . Fuzzy forecasting based on fuzzy time series [J ] . International Journal of Computer Mathematics , 2004 , 81 ( 7 ): 781 - 789 .

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