Forecasting Indonesian inflation using a hybrid ARIMA-ANFIS

Nina Fitriyati, Mahmudi Mahmudi, Madona Yunita Wijaya, Maysun Maysun

Abstract


This paper discusses the prediction of the inflation rate in Indonesia. The data used in this research is assumed to have both linear and non-linear components. The ARIMA model is selected to accommodate the linear component, while the ANFIS method accounts for the non-linear component in the inflation data. Thus, the model is known as the hybrid ARIMA-ANFIS model. The clustering method is performed in the ANFIS model using Fuzzy C-Mean (FMS) with a Gaussian membership function. Consider 2 to 6 clusters. The optimal number of clusters is assessed according to the minimum value of the error prediction. To evaluate the performance of the fitted hybrid ARIMA-ANFIS model, it can be compared to the classical ARIMA model and with the ordinary ANFIS model. The result reveals that the best ARIMA model for inflation prediction in Indonesia is ARIMA(2,1,0). In the hybrid ARIMA(2,1,0)-ANFIS model, two clusters are optimal. Meanwhile, the optimum number of clusters in the ordinary ANFIS model is six. The comparison of prediction accuracy confirms that the hybrid model is superior to the individual model alone of either ARIMA or ANFIS model.


Keywords


Clusters; Gaussian Membership Function; Fuzzy C Means; Linear Components; Non-Linear Components.

Full Text:

PDF

References


Bank Indonesia. (2022). Data inflasi. https://www.bi.go.id/id/statistik/indikator/data-inflasi.aspx

Barak, S., & Sadegh, S. S. (2016). Forecasting energy consumption using ensemble arima-anfis hybrid algorithm. International Journal of Electrical Power and Energy Systems, 82. https://doi.org/10.1016/j.ijepes.2016.03.012

Benmouiza, K., & Cheknane, A. (2019). Clustered anfis network using fuzzy c-means, subtractive clustering, and grid partitioning for hourly solar radiation forecasting. Theoretical and Applied Climatology, 137(1–2). https://doi.org/10.1007/s00704-018-2576-4

Charemza, W., Díaz, C., & Makarova, S. (2019). Conditional term structure of inflation forecast uncertainty: The copula approach. Romanian Journal of Economic Forecasting, 22(1).

Cryer, J. D., & Chan, K.-S. (2008). Time series analysis: With applications in R. In Design (2nd ed., Issue January). Springer Science.

Diarsih, I. H., Tarno, & Rusgiyono, A. (2019). Modeling of red onion production in central java using hybrid arima-anfis. Journal of Physics: Conference Series, 1217(1). https://doi.org/10.1088/1742-6596/1217/1/012080

Enke, D., & Mehdiyev, N. (2014). A hybrid neuro-fuzzy model to forecast inflation. Procedia Computer Science, 36(C). https://doi.org/10.1016/j.procs.2014.09.088

Faulina, R., & Suhartono. (2013). Hybrid arima-anfis for rainfall prediction in indonesia. International Journal of Science and Research, 2(2).

Fauzia, M. (2021). Inflasi 2020 1.68 persen terendah sepanjang sejarah. kompas.com.: https://money.kompas.com/read/2021/01/04/125828426/inflasi-2020-168-persen-terendah-sepanjang-sejarah?page=all

Fitriyati, N., & Wijaya, M. Y. (2022). A monte carlo simulation study to assess estimation methods in cfa on ordinal data. CAUCHY: Jurnal Matematika Murni Dan Aplikasi, 7(3), 332–344.

Hillmer, S. C., & Wei, W. W. S. (1991). Time series analysis: Univariate and multivariate methods. Journal of the American Statistical Association, 86(413). https://doi.org/10.2307/2289741

Işığıçok, E., Öz, R., & Tarkun, S. (2020). Forecasting and technical comparison of inflation in turkey with box-jenkins (arima) models and the artificial neural network. International Journal of Energy Optimization and Engineering, 9(4). https://doi.org/10.4018/ijeoe.2020100106

John, J., Singh, S. R., & Kapur, M. (2020). Inflation forecast combinations – the indian experience.

Massey, F. J. (1951). The kolmogorov-smirnov test for goodness of fit. Journal of the American Statistical Association, 46(253). https://doi.org/10.1080/01621459.1951.10500769

Nau, R. (2014). Notes on linear regression analysis. https://people.duke.edu/~rnau/Notes_on_linear_regression_analysis--Robert_Nau.pdf

Reserve Bank of India. (2017). Monetary policy report.

Tarno, Subanar, Rosadi, D., & Suhartono. (2013). Analysis of financial time series data using adaptive neuro fuzzy inference system (anfis). IJCSI: International Journal of Computer Science, 10(2).

Wen, Y. C., Tan, L. N., & Wuc, H. L. (2014). Inflation forecast based on bp neural network model. Advanced Materials Research, 989–994. https://doi.org/10.4028/www.scientific.net/AMR.989-994.5536

Zhang, B. (2019). Real-time inflation forecast combination for time-varying coefficient models. Journal of Forecasting, 38(3). https://doi.org/10.1002/for.2563




DOI: http://dx.doi.org/10.24042/djm.v5i3.14093

Refbacks

  • There are currently no refbacks.


Copyright (c) 2022 Desimal: Jurnal Matematika

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

  Creative Commons License
Desimal: Jurnal Matematika is licensed under a Creative Commons Attribution-ShareAlike 4.0 International License.