Time Series Fuzzy pada Peramalan Konstribusi Pengeluaran Konsumsi Rumah Tangga terhadap PDRB Kabupaten Majene
Abstract
Time series fuzzy adalah metode peramalan yang didasarkan pada kerangka teori himpunan fuzzy. Metode ini dapat digunakan untuk peramalan dengan data historis bernilai linguistik. Dalam paper ini dibahas konsep dasar time series fuzzy beserta dengan algoritmanya. Pada bagian akhir paper ini, diberikan ilustrasi penerapan time series fuzzy dengan menggunakan data konstribusi pengeluaran konsumsi rumah tangga terhadap PDRB kabupaten Majene.
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References
Basyigit, A. I, Ulu, C, Guzelkaya, M, 2014, A New Fuzzy Time Series Model using Triangular and Trapezoidal Membership Function, Proceeding ITISE 2014 Granada 25 – 27 Juni 2014, p.634 – 644.
BPS Kab. Majene, 2020, KABUPATEN MAJENE DALAM ANGKA MAJENE REGENCY in Figures 2020, ISBN: 978-602-6446-67-1
C.H. Cheng, T.L. Chen & C.H. Chiang, 2006, Trend-weighted fuzzy time series model for TAIEX forecasting, ICONIP, Part III, LNNC 4234, 469-477. DOI: https://doi.org/10.1007/11893295_52
C.H.L. Lee, A. Liu & W.S. Chen, 2006, Pattern Discovery of Fuzzy time series for financial prediction, IEEE Transactions on Knowledge and data Engineering, 18, p.613-625. DOI: https://doi.org/10.1109/TKDE.2006.80
H-K. Yu, 2005, Weighted fuzzy time series models for Taiex forecasting, Physica A, 349, 609-624. DOI: https://doi.org/10.1016/j.physa.2004.11.006
H.H Chu, T.L. Chen, C.H. Cheng & C.C., 2009, Huang, Fuzzy dual-factor time series for stock index forecasting, Expert Systems with Applications, 36, p.165-171. DOI: https://doi.org/10.1016/j.eswa.2007.09.037
K. Huarng, 2001, Heuristic models of fuzzy time series for forecasting, Fuzzy Sets and Systems, 123, p.369-386. DOI: https://doi.org/10.1016/S0165-0114(00)00093-2
K. Huarng, Tiffany H-K Yu & Yu W-S, 2007, A multivariate heuristic model for fuzzy time series forecasting, IEEE Transactions on Systems, Man, and Cybernetics, 37, p.263-275. DOI: https://doi.org/10.1109/TSMCB.2006.890303
Lee, M. H, Efendi, R, Ismail, Z., 2009, Modified Weighted for Enrollment Forecasting Based on Fuzzy Time Series, MATEMATIKA, Volume 25, Number 1, 67–78
Mutalib, S. M, Ramli, N, Daud, M, 2018, Forecasting Fuzzy Time Series Model based on Trapezoidal Fuzzy Numbers with Area and Height Similarity Measure Concept, AIP Conference Proceeding 1974 DOI: https://doi.org/10.1063/1.5041571
Q, Song & B. S. Chissom, 1993, Forecasting Enrolments with Fuzzy Time Series – Part I, Fuzzy Sets and Systems 54, 1-9. DOI: https://doi.org/10.1016/0165-0114(93)90355-L
Ramli, N, Mutalib, S. M, Daud, M, 2018, Fuzzy Time Series Forecasting Model based on Center of Gravity Similarity Measure, Journal of Computer Science & Computational Mathematics, 8(4), p.121 – 124. DOI: https://doi.org/10.20967/jcscm.2018.04.010
S.M. Chen, 2000, Temperature prediction using fuzzy time series, IEEE Transactions on Systems, Man, and Cybernetics, 30, p.263-275. DOI: https://doi.org/10.1109/3477.836375
S.M. Chen, 1996, Forecasting enrolments based on fuzzy time series, Fuzzy Sets and Systems, 81, p.311-319. DOI: https://doi.org/10.1016/0165-0114(95)00220-0
Tsaur, R. C, 2012, A Fuzzy Time Series-Markov Chain Model with an Application to Forecast the Exchange Rate between the Taiwan and US Dollar, International Journal of Innovative Computing, Information and Control, vol. 8, No. 7, p.4931 – 4942.
T.A Jilani & S.M.A. Burney, 2008, A refined fuzzy time series model for stock market forecasting, Physica A, ScienceDirect, 387, p.2857-2862. DOI: https://doi.org/10.1016/j.physa.2008.01.099
T.H.K. Yu & K.H. Huarng, 2008, A bivariate fuzzy time series model to forecast the TAIEX, Expert Systems with Application 34, 2945-2952. DOI: https://doi.org/10.1016/j.eswa.2007.05.016
Xihao, S and Yimin, L., 2008, Average Based Fuzzy Time Series Models for Forecasting Shanghai Compound Index, World Journal of Modelling and Simulation, 4(2), 104 – 111.
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