Optimization of ARIMA Forecasting Model using Firefly Algorithm

https://doi.org/10.22146/ijccs.37666

Ilham unggara(1*), Aina Musdholifah(2), Anny Kartika Sari(3)

(1) Master Program of Computer Science; FMIPA UGM, Yogyakarta
(2) Department of Computer Science and Electronics, FMIPA UGM, Yogyakarta
(3) Department of Computer Science and Electronics, FMIPA UGM, Yogyakarta
(*) Corresponding Author

Abstract


 Time series prediction aims to control or recognize the behavior of the system based on the data in a certain period of time. One of the most widely used method in time series prediction is ARIMA (Autoregressive Integrated Moving Average). However, ARIMA has a weakness in determining the optimal model. firefly algorithm is used to optimize ARIMA model (p, d, q). by finding the smallest AIC (Akaike Information Criterion) value in determining the best ARIMA model. The data used in the study are daily stock data JCI period January 2013 until August 2016 and data of foreign tourist visits to Indonesia period January 1988 to November 2017.Based on testing, for JCI data, obtained predicted results with Box-Jenkins ARIMA model produces RMSE 49.72, whereas the prediction with the ARIMA Optimization model yielded RMSE 49.48. For the data of Foreign Tourist Visits, the predicted results with the Box-Jenkins ARIMA model resulted in RMSE 46088.9, whereas the predicted results with ARIMA optimization resulted in RMSE 44678.4. From these results it can be concluded that the optimization of ARIMA model with Firefly Algorithm produces better forecasting model than ARIMA model without Optimization.


Keywords


Optimization; Forecasting; ARIMA; Firefly Algorithm; AIC; RMSE

Full Text:

PDF


References

 [1]      Subanar. 2013. Statistika Matematika. Graha Ilmu. Yogyakarta.

 [2]      Yolanda, S. 2015. Perbandingan kinerja beberapa model peramalan runtut waktu untuk variabel kurs di indonesia dengan mengunakan model Box Jenkins  Arima, Arch        dan Garch  periode 3 Januari 2000 - 7 Juli 2014. Tesis, Universitas Gadjah Mada

 [3]      Hikichi, E., Salgado, E. G., and Beijo, L. E. 2017. “Forecasting number of ISO 14001 certifications in the Americas using ARIMA models”, Journal of Cleaner Production. Vol. 147. Pages 242-253.

 [4]      Chen, K. 2011. “Combining linier and nonlinier model in forcasting Foreign Tourist Visit demand” Expert Systems with Applications. Vol. 38. Issue 8: 10368-10376.

 [5]      Fard, A, K. 2014. “A new hybrid Modified Firefly Algorithm and Support Vector Regression model for accurate Short Term Load Forecasting”. Expert Systems with Applications. Vol. 41 (2014) 6047–6056.

 [6]      Deo, R, C. 2017. “Multi-layer perceptron hybrid model integrated with the firefly optimizer algorithm for windspeed prediction of target site using a limited set of neighboring reference station data”, Renewable Energy 116 (2018) 309-323

 [7]      Yang, X, S. 2018. "Nature-Inspired Algorithms and Applied Optimization" ISBN 978-3-319-67668-5

 [8]      Didi, R. 2014. Analisis Runtun Waktu dan Aplikasinya dengan R. Gadjah   Mada       University Press. Yogyakarta.

 [9]        Indeks Harga Saham Gabungan, (IHSG) Januari 2013 sampai dengan Agustus 2016, yaitu selama 888 hari, https://finance.yahoo.com/quote/%5EJKSE/.

[10]       Badan Pusat Statistik, kunjungan wisatawan mancanegara ke Indonesia periode Januari 1988 sampai dengan November 2017, yaitu selama 359 bulan, https://www.bps.go.id/subject/16/pariwisata.html.

[11]     S, L, Tilahun and J, M, T, Ngnotchouye, "Firefly Algorithm for Discrete Optimization Problems: A Survey" KSCE Journal of Civil Engineering. pISSN 1226-7988, eISSN 1976-3808

[12]     Lingga, P. 2016. Model hybrid ARIMA-SVR untuk peramalan data runtun watku.      Universitas Gadjah Mada.

[13]     Sen, P., Roy, M., and Pal, P. 2016. Application of ARIMA for forecasting energy consumption and GHG emission: A case study of an Indian pig iron manufacturing organization. Energy. Vol. 116. Part 1. Pages 1031-1038.

[14]     Nebojsa, B., Brajevic, I and Tuba M,. 2013. Firefly Algorithm Applied to Integer Programming Problems. Faculty of Computer Science, Megatrend University Belgrade, ISBN: 978-1- 61804-158-6 143



DOI: https://doi.org/10.22146/ijccs.37666

Article Metrics

Abstract views : 952 | views : 569

Refbacks

  • There are currently no refbacks.




Copyright (c) 2019 IJCCS (Indonesian Journal of Computing and Cybernetics Systems)

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



Copyright of :
IJCCS (Indonesian Journal of Computing and Cybernetics Systems)
ISSN 1978-1520 (print); ISSN 2460-7258 (online)
is a scientific journal the results of Computing
and Cybernetics Systems
A publication of IndoCEISS.
Gedung S1 Ruang 416 FMIPA UGM, Sekip Utara, Yogyakarta 55281
Fax: +62274 555133
email:ijccs.mipa@ugm.ac.id | http://jurnal.ugm.ac.id/ijccs



View My Stats1
View My Stats2