DOI: https://doi.org/10.14710/medstat.13.2.116-124

SELECTION OF INPUT VARIABLES OF NONLINEAR AUTOREGRESSIVE NEURAL NETWORK MODEL FOR TIME SERIES DATA FORECASTING

*Hermansah Hermansah scopus  -  Mathematics Education Study Program, Riau Kepulauan University, Indonesia
Dedi Rosadi  -  Mathematics Study Program, Gadjah Mada University, Indonesia
Abdurakhman Abdurakhman  -  Mathematics Study Program, Gadjah Mada University, Indonesia
Herni Utami  -  Mathematics Study Program, Gadjah Mada University, Indonesia
Open Access Copyright (c) 2020 MEDIA STATISTIKA under http://creativecommons.org/licenses/by-nc-sa/4.0.

Citation Format:
Abstract
NARNN is a type of ANN model consisting of a limited number of parameters and widely used for various applications. This study aims to determine the appropriate NARNN model, for the selection of input variables of nonlinear autoregressive neural network model for time series data forecasting, using the stepwise method. Furthermore, the study determines the optimal number of neurons in the hidden layer, using a trial and error method for some architecture. The NARNN model is combined in three parts, namely the learning method, the activation function, and the ensemble operator, to get the best single model. Its application in this study was conducted on real data, such as the interest rate of Bank Indonesia. The comparison results of MASE, RMSE, and MAPE values with ARIMA and Exponential Smoothing models shows that the NARNN is the best model used to effectively improve forecasting accuracy.
Fulltext View|Download
Keywords: Stepwise Method; Learning Method; Activation Function; Ensemble Operator; NARNN Model

Article Metrics:

Article Info
Section: Articles
Language : EN
Recent articles
Front Matter Vol. 13 No. 2 2020 MODELING EAST JAVA INDONESIA LIFE EXPECTANCY USING SEMIPARAMETRIC REGRESSION MIXED SPLINE TRUNCATED AND FOURIER SERIES IDENTIFICATION OF RAINFALL DISTRIBUTION IN WEST SUMATERA AND ASSESSMENT OF ITS PARAMETERS USING BAYES METHOD A SIMULATION STUDY OF FIXED-B ASYMPTOTIC DISTRIBUTIONS IN LINEAR PANEL MODELS WITH FIXED EFFECTS More recent articles
Most cited articles
IDENTIFIKASI AUTOKORELASI SPASIAL PADA JUMLAHPENGANGGURAN DI JAWA TENGAH MENGGUNAKAN INDEKS MORAN ANALISIS PENGARUH KARAKTERISTIK WILAYAH (KELURAHAN) TERHADAP BANYAKNYA KASUS DEMAM BERDARAH DENGUE (DBD) DI KOTA SEMARANG PEMILIHAN VARIABEL PADA MODEL GEOGRAPHICALLY WEIGHTED REGRESSION MODEL REGRESI COX PROPORSIONAL HAZARD PADA DATA KETAHANAN HIDUP Front-Matter More cited articles
  1. Abraham, B., & Ledolter, J. (2005). Statistical Methods for Forecasting. New York: John Wiley and Sons
  2. Anastasiadis, A. D., Magoulas, G. D., & Vrahatis, M. N. (2005). New Globally Convergent Training Scheme Based on the Resilient Propagation Algorithm. Neurocomputing, 64, 253–270
  3. Crone, S. F., & Kourentzes, N. (2010). Feature Selection for Time Series Prediction - A Combined Filter and Wrapper Approach for Neural Networks. Neurocomputing, 73(10), 1923–1936
  4. Fausett, L. (1994). Fundamentals of Neural Networks: Architectures, Algorithms, and Applications. United States: Prentice-Hall
  5. Hyndman, R. J., & Khandakar, Y. (2008). Automatic Time Series Forecasting: The Forecast Package for R. Journal of Statistical Software, 27(3), 1–22
  6. Kourentzes, N. (2019). Package nnfor: Time Series Forecasting with Neural Networks. Retrieved from https://cran.r-project.org/web/packages/nnfor/nnfor.pdf
  7. Kourentzes, N., Barrow, D. K., & Crone, S. F. (2014). Neural Network Ensemble Operators for Time Series Forecasting. Expert Systems with Applications, 41(9), 4235–4244
  8. Riedmiller, M., & Braun, H. (1993). A Direct Adaptive Method for Faster Backpropagation Learning: The RPROP Algorithm. IEEE International Conference on Neural Networks, 1, 586–591
  9. Samarasinghe, S. (2007). Neural Networks for Applied Sciences and Engineering: From Fundamentals to Complex Pattern Recognition. New York: Auerbach Publications
  10. Sembiring, R. K. (1995). Analisis Regresi. Bandung: ITB
  11. Suhartono, Subanar, & Guritno, S. (2006). Model Selection in Neural Networks by Using Inference of R2Incremental, PCA, and SIC Criteria for Time Series Forecasting. Journal of Quantitative Methods: Journal Devoted to The Mathematical and Statistical Application in Various Fields, 2(1), 41–57
  12. Walczak, S. (2001). An Empirical Analysis of Data Requirements for Financial Forecasting with Neural Networks. Journal of Management Information Systems, 17(4), 203–222
  13. Warner, B., & Misra, M. (1996). Understanding Neural Networks as Statistical Tools. The American Statistician, 50(4), 284–293
  14. Yeung, D. S., Cloete, I., Shi, D., & Ng, W. W. Y. (1998). Sensitivity Analysis of Neural Networks. New York: Springer
  15. Zhang, G. P. (2003). Time Series Forecasting using A Hybrid ARIMA and Neural Network Model. Neurocomputing, 50, 159–175

Last update:

  1. Enhanced Neural Network-Based Univariate Time-Series Forecasting Model for Big Data

    Suyel Namasudra, S. Dhamodharavadhani, R. Rathipriya, Ruben Gonzalez Crespo, Nageswara Rao Moparthi. Big Data, 12 (2), 2024. doi: 10.1089/big.2022.0155

  2. Differencing effect in series-parallel architecture of NARX model for time series forecasting

    Hermansah, Dedi Rosadi, Abdurakhman, Herni Utami, Gumgum Darmawan. 7TH INTERNATIONAL CONFERENCE ON MATHEMATICS: PURE, APPLIED AND COMPUTATION: Mathematics of Quantum Computing, 2641 , 2022. doi: 10.1063/5.0117393

  3. A GA-BP neural network for nonlinear time-series forecasting and its application in cigarette sales forecast

    Zheng Sun, XiNa Li, HongTao Zhang, Mohammad Asif Ikbal, Ataur Rahman Farooqi. Nonlinear Engineering, 11 (1), 2022. doi: 10.1515/nleng-2022-0025

Last update: 2024-10-31 22:01:45

No citation recorded.