The data forecasting of plant equipment plays an important role in assurance of the safe and reliable
operation of the plant equipment. Thus, it is necessary to improve the accuracy of data forecasting of the
equipment. A new two-factor fuzzy time series algorithm is proposed to forecast the data of the plant equipment.
This method not only overcomes the limitations of one factor fuzzy time series algorithm, but also overcomes the
drawbacks of traditional two-factor fuzzy time series algorithm. The collected data is used in the power plant to
conduct experiments, where the metrics is Mean Absolute Percentage Error (MAPE). The results show that this
method is superior to the existing two-factor fuzzy time series algorithms, and yields good results in the equipment
prediction.
HE Shanhong (何善红), RONG Baizhong (荣百中), QU Meng (瞿勐), WANG Shuangfei (王双飞), LI Huanhuan (李欢欢), WANG Fengyang (王冯阳), WU Jin (吴进)
. Two-Factor Fuzzy Time Series for Equipment Data Prediction[J]. Journal of Shanghai Jiaotong University(Science), 2018
, 23(5)
: 684
-690
.
DOI: 10.1007/s12204-018-1983-0
[1] YU Q, YIN Y, YANG G, et al. Face andgait recognition based on semi-supervised learning[C]//Proceedings of the 2012 Chinese Conference onPattern Recognition. Beijing, China: Springer, 2012:284-291.
[2] KONONENKO I. Machine learning for medical diagnosis:History, state of the art and perspective [J]. ArtificialIntelligence in Medicine, 2001, 23(1): 89-109.
[3] KIRKOS E, SPATHIS C, MANOLOPOULOS Y. Datamining techniques for the detection of fraudulent financialstatements [J]. Expert Systems with Applications,2007, 32(4): 995-1003.
[4] ZADEH L A. Fuzzy sets [J]. Information and Control,1965, 8(3): 338-353.
[5] SONG Q, CHISSOM B S. Forecasting enrollmentswith fuzzy time series: Part I [J]. Fuzzy Sets and Systems,1993, 54(1): 1-9.
[6] SONG Q, CHISSOM B S. Forecasting enrollmentswith fuzzy time series: Part II [J]. Fuzzy Sets and Systems,1994, 62(1): 1-8.
[7] RAFIEI D. On similarity-based queries for time seriesdata [C]// Proceedings of the 15th InternationalConference on Data Engineering. Sydney, Australia:IEEE, 1999: 410-417.
[8] CHEN S M, HWANG J R. Temprature prediction usingfuzzy time series [J]. IEEE Transactions on Systems,Man, and Cybernetics — Part B: Cybernetics,2000, 30(2): 263-275.
[9] YU W L, FANG J W, LIAO J P. A novel FCM-baseddeterministic forecasting model for fuzzy time series[J]. Computer Engineering & Science, 2010, 32(7):112-116.
[10] WU B, LIN Y C. Fuzzy time series analysis and forecasting:With an example to Taiwan weighted stockindex [J]. Acta Mathematicae Applicatae Sinica, 2002,25(1): 67-76 (in Chinese).
[11] BOX J E P, JENKINS G M. Time series analysis, forecastingand control [M]. 4th ed. New York, USA: JohnWileyd & Sons, 2008.
[12] SONG Q, CHISSOM B S. Fuzzy time series and itsmodels [J]. Fuzzy Sets and Systems, 1993, 54(3): 269-277.
