FORECASTING NICKEL PRICES WITH THE AUTOMATIC CLUSTERING FUZZY TIME SERIES MARKOV APPROACH

  • M. Al Haris Department of Statistics, Faculty of Agricultural Science and Technology, Universitas Muhammadiyah Semarang, Indonesia https://orcid.org/0000-0003-3702-3161
  • Wulan Sari Department of Statistics, Faculty of Agricultural Science and Technology, Universitas Muhammadiyah Semarang, Indonesia https://orcid.org/0009-0001-6480-605X
  • Fatkhurokhman Fauzi Department of Statistics, Faculty of Agricultural Science and Technology, Universitas Muhammadiyah Semarang, Indonesia https://orcid.org/0000-0002-8277-8638
  • Muhammad Sam'an Department of Informatics, Faculty of Engineering and Computer Science, Universitas Muhammadiyah Semarang, Indonesia https://orcid.org/0000-0001-5408-5562
DOI: https://doi.org/10.30598/barekengvol19iss2pp1237-1250
Keywords: Automatic Clustering, Fuzzy Time Series Markov Chain, Forecasting, Nickel Price

Abstract

Nickel was a critical raw material used in a wide range of industries. The price movement of nickel tends to fluctuate and remain uncertain due to market conditions varying over time. Therefore, forecasting nickel prices was essential to understanding future price movements. In this study, we applied the automatic clustering fuzzy time series Markov chain method. The automatic clustering algorithm generates multiple intervals and fuzzy relations. Subsequently, forecasting was based on these fuzzy relations and a Markov chain transition probability matrix involving three stages to enhance forecast accuracy. We use monthly closing futures nickel price data from January 2009 to May 2024. The accuracy of the forecasting model was measured using the mean absolute percentage error (MAPE). The analysis showed that implementing the automatic clustering fuzzy time series Markov chain method results in excellent forecasting accuracy, with a MAPE value of 1.76% (equivalent to 98.24% accuracy). The predicted nickel price for June 2024 was US$ 19,608.5.

Downloads

Download data is not yet available.

References

M. Pouresmaieli, M. Ataei, A. Nouri Qarahasanlou, and A. Barabadi, “INTEGRATION OF RENEWABLE ENERGY AND SUSTAINABLE DEVELOPMENT WITH STRATEGIC PLANNING IN THE MINING INDUSTRY,” Results Eng., vol. 20, no. August, p. 101412, 2023, doi: 10.1016/j.rineng.2023.101412.

S. Salinita and A. Nugroho, “LATERITIC NICKEL ORE MODELLING FOR RESERVES ESTIMATION AT PT. ANUGERAH TOMPIRA NIKEL IN THE MASAMA AREA, BANGGAI REGENCY,” J. Teknol. Miner. dan Batubara, vol. 10, no. 2, pp. 54–68, 2014, doi: https://doi.org/10.30556/jtmb.Vol10.No2.2014.737.

D. D. Radhica and R. A. A. Wibisana, “PROTEKSIONISME NIKEL INDONESIA DALAM PERDAGANGAN DUNIA,” Cendekia Niaga, vol. 7, no. 1, pp. 74–84, 2023, doi: 10.52391/jcn.v7i1.821.

Kementerian Perdagangan Republik Indonesia, “TRADE POST: TRANSISI ENERGI DAN PENINGKATAN PERMINTAAN KOMODITAS LOGAM SERTA POSISI INDONESIA SEBAGAI PRODUSEN NIKEL DUNIA,” Badan Kebijakan Perdagangan, vol. II, pp. 34–39, 2023. [Online]. Available: https://bkperdag.kemendag.go.id/media_content/2023/06/tradepost__20230627112551tradeposte-magazineedisijuni2023.pdf

Aprisal and A. M. Abadi, “FUZZY SISTEM: ESTIMASI HARGA NIKEL DUNIA,” JUMLAHKU J. Mat. Ilm. STKIP Muhammadiyah Kuningan, vol. 5, no. 1, pp. 48–58, 2019, doi: https://doi.org/10.33222/jumlahku.v5i1.583.

Nurbaiti, M. S. Boedoyo, and P. Yusgiantoro, “PENGELOLAAN NIKEL INDONESIA TERHADAP PERTAHANAN NEGARA DAN KETAHANAN ENERGI,” J. Ketahanan Energi, vol. 8, no. 2, pp. 33–52, 2022, [Online]. Available: https://jurnalprodi.idu.ac.id/index.php/KE/article/view/4465

S. Swarup and G. S. Kushwaha, “NICKEL AND COBALT PRICE VOLATILITY FORECASTING USING A SELF-ATTENTION-BASED TRANSFORMER MODEL,” Appl. Sci., vol. 13, no. 8, 2023, doi: 10.3390/app13085072.

E. N. Sofiyanti, S. Ulinuha, R. Okiyanto, M. Al Haris, and R. Wasono, “PERAMALAN HARGA EMAS MENGGUNAKAN METODE FUZZY TIME SERIES CHEN DALAM INVESTASI UNTUK MEMINIMALISIR RISKO,” J. Math. Comput. Stat., vol. 7, no. 1, pp. 55–66, 2024, doi: https://doi.org/10.35580/jmathcos.v7i1.1955.

R. Wasono, Y. Fitri, and M. Al Haris, “FORECASTING THE NUMBER OF AIRPLANE PASSENGERS USING HOLT WINTER’S EXPONENTIAL SMOOTHING METHOD AND EXTREME LEARNING MACHINE METHOD,” BAREKENG J. Ilmu Mat. dan Terap., vol. 18, no. 1, pp. 0427–0436, 2024, doi: 10.30598/barekengvol18iss1pp0427-0436.

I. F. Amri, W. Sari, V. A. Widyasari, N. Nurohmah, and M. Al Haris, “THE ARIMA-GARCH METHOD IN CASE STUDY FORECASTING THE DAILY STOCK PRICE INDEX OF PT. JASA MARGA (PERSERO),” Eig. Math. J., vol. 7, no. 1, pp. 25–33, 2024, doi: 10.29303/emj.v7i1.174.

Y. Ekananta, L. Muflikhah, and C. Dewi, “PENERAPAN METODE AVERAGE-BASED FUZZY TIME SERIES UNTUK PREDIKSI KONSUMSI ENERGI LISTRIK INDONESIA,” J. Pengemb. Teknol. Inf. dan Ilmu Komput., vol. 2, no. 3, pp. 1283–1289, 2018, [Online]. Available: https://j-ptiik.ub.ac.id/index.php/j-ptiik/article/view/1126

A. R. Siregar, R. F. Sari, and R. Widyasari, “FORECASTING THE NUMBER OF COVID-19 SUFFERERS IN NORTH SUMATRA USING THE AUTOMATIC CLUSTERING FUZZY TIME SERIES MARKOV CHAIN METHOD,” J. Math. Sci. Comput. with Appl., vol. 2, no. 1, pp. 46–54, 2021, [Online]. Available: https://pcijournal.org/index.php/jmscowa/article/view/48

Y. P. Anggodo and W. F. Mahmudy, “PERAMALAN BUTUHAN HIDUP MINIMUM MENGGUNAKAN AUTOMATIC CLUSTERING DAN FUZZY LOGICAL RELATIONSHIP,” J. Teknol. Inf. dan Ilmu Komput., vol. 3, no. 2, p. 94, 2016, doi: 10.25126/jtiik.201632202.

Y. Alyousifi, M. Othman, A. Husin, and U. Rathnayake, “A NEW HYBRID FUZZY TIME SERIES MODEL WITH AN APPLICATION TO PREDICT PM10 CONCENTRATION,” Ecotoxicol. Environ. Saf., vol. 227, p. 112875, 2021, doi: 10.1016/j.ecoenv.2021.112875.

E. Haryono, A. Widodo, and S. Abusini, “KAJIAN MODEL AUTOMATIC CLUSTERING-FUZZY TIME SERIES-MARKOV CHAIN DALAM MEMPREDIKSI DATA HISTORIS JUMLAH KECELAKAAN LALU LINTAS DI KOTA MALANG,” J. Sains Dasar, vol. 2, no. 1, pp. 63–71, 2013, doi: 10.21831/jsd.v2i1.3365.

I. N. Hidayati, M. Al Haris, and T. W. Utami, “METODE AVERAGE BASED FUZZY TIME SERIES MARKOV CHAIN PADA DATA LAJU INFLASI DI INDONESIA AVERAGE,” in Prosiding Seminar Nasional UNIMUS, 2022, pp. 580–597. [Online]. Available: https://prosiding.unimus.ac.id/index.php/semnas/article/view/1211/1208

S. Damayanti, S. Yosmar, and N. Afandi, “IMPLEMENTATION OF FUZZY TIME SERIES CHEN FOR FORECASTING INDONESIAN OIL AND GAS IMPORTS VALUE,” BAREKENG J. Ilmu Mat. dan Terap., vol. 17, no. 2, pp. 0685–0694, 2023, doi: 10.30598/barekengvol17iss2pp0685-0694.

S. Damayanti, J. Rizal, S. Yosmar, N. Afandi, and V. Acnesya, “EARTHQUAKE FREQUENCY DATA MODELING IN MENTAWAI USING FUZZY TIME SERIES LEE AND FUZZY TIME SERIES TSAUR,” BAREKENG J. Ilmu Mat. dan Terap., vol. 18, no. 1, pp. 0281–0294, 2024, doi: 10.30598/barekengvol18iss1pp0281-0294.

D. Devianto, K. Ramadani, Maiyastri, Y. Asdi, and M. Yollanda, “THE HYBRID MODEL OF AUTOREGRESSIVE INTEGRATED MOVING AVERAGE AND FUZZY TIME SERIES MARKOV CHAIN ON LONG-MEMORY DATA,” Front. Appl. Math. Stat., vol. 8, 2022, doi: 10.3389/fams.2022.1045241.

X. Zeng, L. Shu, and J. Jiang, “FUZZY TIME SERIES FORECASTING BASED ON GREY MODEL AND MARKOV CHAIN,” IAENG Int. J. Appl. Math., vol. 46, no. 4, pp. 464–472, 2016, [Online]. Available: https://www.iaeng.org/IJAM/issues_v46/issue_4/IJAM_46_4_08.pdf

R. A. Kafi, Y. R. Safitri, Y. Widyaningsih, and B. D. Handari, “COMPARISON OF WEIGHTED MARKOV CHAIN AND FUZZY TIME SERIES MARKOV CHAIN IN FORECASTING STOCK CLOSING PRICE OF COMPANY X,” in AIP Conference Proceedings, 2019. doi: 10.1063/1.5132460.

A. B. Salsabila, F. Firdaniza, B. N. Ruchjana, and A. S. Abdullah, “PYTHON SCRIPT FUZZY TIME SERIES MARKOV CHAIN MODEL FOR FORECASTING THE NUMBER OF DISEASES COCOA PLANT IN BENDUNGAN DISTRICT,” Int. J. Data Netw. Sci., vol. 7, no. 2, pp. 627–636, 2023, doi: 10.5267/j.ijdns.2023.3.009.

N. Didenko, “MODELING THE GLOBAL NICKEL MARKET WITH A TRIANGULAR SIMULTANEOUS EQUATIONS MODEL,” Int. J. Syst. Assur. Eng. Manag., vol. 11, no. S1, pp. 119–129, May 2020, doi: 10.1007/s13198-019-00936-0.

Published
2025-04-01
How to Cite
[1]
M. A. Haris, W. Sari, F. Fauzi, and M. Sam’an, “FORECASTING NICKEL PRICES WITH THE AUTOMATIC CLUSTERING FUZZY TIME SERIES MARKOV APPROACH”, BAREKENG: J. Math. & App., vol. 19, no. 2, pp. 1237-1250, Apr. 2025.
Issue
Vol 19 No 2 (2025): BAREKENG: Journal of Mathematics and Its Application
Section
Articles

Copyright (c) 2025 M. Al Haris, Wulan Sari, Fatkhurokhman Fauzi, Muhammad Sam'an

Creative Commons License

This work is licensed under a Creative Commons Attribution-ShareAlike 4.0 International License.

Authors who publish with this Journal agree to the following terms:

  1. Author retain copyright and grant the journal right of first publication with the work simultaneously licensed under a creative commons attribution license that allow others to share the work within an acknowledgement of the work’s authorship and initial publication of this journal.
  2. Authors are able to enter into separate, additional contractual arrangement for the non-exclusive distribution of the journal’s published version of the work (e.g. acknowledgement of its initial publication in this journal).
  3. Authors are permitted and encouraged to post their work online (e.g. in institutional repositories or on their websites) prior to and during the submission process, as it can lead to productive exchanges, as well as earlier and greater citation of published works.