GSTARIMA Model with Missing Value for Forecasting Gold Price

Authors

  • Fadhlul Mubarak Department of Statistics, EskiÅŸehir Technical University, EskiÅŸehir, Turkey
  • Atilla Aslanargun Department of Statistics, EskiÅŸehir Technical University, EskiÅŸehir, Turkey
  • Ä°lyas Sıklar Department of Economics, Anadolu University, EskiÅŸehir, Turkey

DOI:

https://doi.org/10.29244/ijsa.v6i1p90-100

Keywords:

GSTAR(1), GSTARI (1, 1), imputation technique, RMSE

Abstract

Gold is one of the investments that be a great demand. Selecting and applying the best GSTARIMA model for gold price forecasting was the aim of this study. However, the gold price data that has been obtained missing values. Missing value data has been imputed by the last data before the missing value and moving average techniques. The GSTAR (1) and GSTARI (1, 1) models have been combined with an imputation technique solved this problem. Based on the smallest RMSE value, the GSTARI (1, 1) model which has been combined with the imputation technique that used the last value was the best method because it produced the smallest RMSE when compared to other methods. Forecasting results shown that gold prices in the United States, United Kingdom, and Indonesia increased but gold prices in Turkey actually decreased. Forecasting gold prices in each of these countries become one of the references in investing in gold. Based on the results of gold price forecasting, gold prices changed but not significantly.

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References

Andayani, N., Sumertajaya, I. M., Ruchjana, B. N., & Aidi, M. N. (2018). Comparison of GSTARIMA and GSTARIMA-X Model by using Transfer Function Model Approach to Rice Price Data. 187, 012052. https://doi.org/10.1088/1755-1315/187/1/012052

Andiojaya, A., & Demirhan, H. (2019). A bagging algorithm for the imputation of missing values in time series. Expert Systems with Applications, 129, 10–26. https://doi.org/10.1016/j.eswa.2019.03.044

Aras, S. (2021). Stacking hybrid GARCH models for forecasting Bitcoin volatility. Expert Systems with Applications, 174, 114747. https://doi.org/10.1016/j.eswa.2021.114747

ArunKumar, K. E., Kalaga, D. V., Sai Kumar, Ch. M., Chilkoor, G., Kawaji, M., & Brenza, T. M. (2021). Forecasting the dynamics of cumulative COVID-19 cases (confirmed, recovered and deaths) for top-16 countries using statistical machine learning models: Auto-Regressive Integrated Moving Average (ARIMA) and Seasonal Auto-Regressive Integrated Moving Average (SARIMA). Applied Soft Computing, 103, 107161. https://doi.org/10.1016/j.asoc.2021.107161

Aulia, N., & Saputro, D. R. S. (2021). Generalized Space Time Autoregressive Integrated Moving Average with Exogenous (GSTARIMA-X) Models. 1808(1), 012052. https://doi.org/10.1088/1742-6596/1808/1/012052

Cinar, Y. G., Mirisaee, H., Goswami, P., Gaussier, E., & Aït-Bachir, A. (2018). Period-aware content attention RNNs for time series forecasting with missing values. Neurocomputing, 312, 177–186. https://doi.org/10.1016/j.neucom.2018.05.090

Dias, G. F., & Kapetanios, G. (2018). Estimation and forecasting in vector autoregressive moving average models for rich datasets. Journal of Econometrics, 202(1), 75–91. https://doi.org/10.1016/j.jeconom.2017.06.022

Fallah, B., Ng, K. T. W., Vu, H. L., & Torabi, F. (2020). Application of a multi-stage neural network approach for time-series landfill gas modeling with missing data imputation. Waste Management, 116, 66–78. https://doi.org/10.1016/j.wasman.2020.07.034

Fan, D., Sun, H., Yao, J., Zhang, K., Yan, X., & Sun, Z. (2021). Well production forecasting based on ARIMA-LSTM model considering manual operations. Energy, 220, 119708. https://doi.org/10.1016/j.energy.2020.119708

Guefano, S., Tamba, J. G., Azong, T. E. W., & Monkam, L. (2021). Methodology for forecasting electricity consumption by Grey and Vector autoregressive models. MethodsX, 8, 101296. https://doi.org/10.1016/j.mex.2021.101296

Hossain, Md. S., Ahmed, S., & Uddin, Md. J. (2021). Impact of weather on COVID-19 transmission in south Asian countries: An application of the ARIMAX model. Science of The Total Environment, 761, 143315. https://doi.org/10.1016/j.scitotenv.2020.143315

Hung, J.-C., Liu, H.-C., & Yang, J. J. (2020). Improving the realized GARCH’s volatility forecast for Bitcoin with jump-robust estimators. The North American Journal of Economics and Finance, 52, 101165. https://doi.org/10.1016/j.najef.2020.101165

Jeong, D., Park, C., & Ko, Y. M. (2021). Short-term electric load forecasting for buildings using logistic mixture vector autoregressive model with curve registration. Applied Energy, 282, 116249. https://doi.org/10.1016/j.apenergy.2020.116249

Koutlis, C., Papadopoulos, S., Schinas, M., & Kompatsiaris, I. (2020). LAVARNET: Neural Network Modeling of Causal Variable Relationships for Multivariate Time Series Forecasting. Applied Soft Computing, 96, 106685. https://doi.org/10.1016/j.asoc.2020.106685

Li, K., Liang, Y., Li, J., Liu, M., Feng, Y., & Shao, Y. (2020). Internet search data could Be used as novel indicator for assessing COVID-19 epidemic. Infectious Disease Modelling, 5, 848–854. https://doi.org/10.1016/j.idm.2020.10.001

Liu, M.-D., Ding, L., & Bai, Y.-L. (2021). Application of hybrid model based on empirical mode decomposition, novel recurrent neural networks and the ARIMA to wind speed prediction. Energy Conversion and Management, 233, 113917. https://doi.org/10.1016/j.enconman.2021.113917

Liu, X., & Lin, Z. (2021). Impact of Covid-19 pandemic on electricity demand in the UK based on multivariate time series forecasting with Bidirectional Long Short Term Memory. Energy, 227(C). https://ideas.repec.org/a/eee/energy/v227y2021ics0360544221007040.html

Liu, Z., & Huang, S. (2021). Carbon option price forecasting based on modified fractional Brownian motion optimized by GARCH model in carbon emission trading. The North American Journal of Economics and Finance, 55, 101307. https://doi.org/10.1016/j.najef.2020.101307

Marchese, M., Kyriakou, I., Tamvakis, M., & Di Iorio, F. (2020). Forecasting crude oil and refined products volatilities and correlations: New evidence from fractionally integrated multivariate GARCH models. Energy Economics, 88, 104757. https://doi.org/10.1016/j.eneco.2020.104757

Paul, S., & Sharma, P. (2021). Forecasting gains by using extreme value theory with realised GARCH filter. IIMB Management Review, 33(1), 64–70. https://doi.org/10.1016/j.iimb.2021.03.011

Quesada, D., Valverde, G., Larrañaga, P., & Bielza, C. (2021). Long-term forecasting of multivariate time series in industrial furnaces with dynamic Gaussian Bayesian networks. Engineering Applications of Artificial Intelligence, 103, 104301. https://doi.org/10.1016/j.engappai.2021.104301

Salisu, A. A., Gupta, R., Bouri, E., & Ji, Q. (2020). The role of global economic conditions in forecasting gold market volatility: Evidence from a GARCH-MIDAS approach. Research in International Business and Finance, 54(C). https://ideas.repec.org/a/eee/riibaf/v54y2020ics0275531920307273.html

Selvaraj, J. J., Arunachalam, V., Coronado-Franco, K. V., Romero-Orjuela, L. V., & Ramírez-Yara, Y. N. (2020). Time-series modeling of fishery landings in the Colombian Pacific Ocean using an ARIMA model. Regional Studies in Marine Science, 39, 101477. https://doi.org/10.1016/j.rsma.2020.101477

Toğa, G., Atalay, B., & Toksari, M. D. (2021). COVID-19 prevalence forecasting using Autoregressive Integrated Moving Average (ARIMA) and Artificial Neural Networks (ANN): Case of Turkey. Journal of Infection and Public Health, 14(7), 811–816. https://doi.org/10.1016/j.jiph.2021.04.015

Vanhoenshoven, F., Nápoles, G., Froelich, W., Salmeron, J., & Vanhoof, K. (2020). Pseudoinverse learning of Fuzzy Cognitive Maps for multivariate time series forecasting. Applied Soft Computing. https://doi.org/10.1016/j.asoc.2020.106461

Xing, D.-Z., Li, H.-F., Li, J.-C., & Long, C. (2021). Forecasting price of financial market crash via a new nonlinear potential GARCH model. Physica A: Statistical Mechanics and Its Applications, 566, 125649. https://doi.org/10.1016/j.physa.2020.125649

Yang, H., Li, X., Qiang, W., Zhao, Y., Zhang, W., & Tang, C. (2021). A network traffic forecasting method based on SA optimized ARIMA–BP neural network. Computer Networks, 193, 108102. https://doi.org/10.1016/j.comnet.2021.108102

Zhang, R., & Jia, H. (2021). Production performance forecasting method based on multivariate time series and vector autoregressive machine learning model for waterflooding reservoirs. Petroleum Exploration and Development, 48(1), 201–211. https://doi.org/10.1016/S1876-3804(21)60016-2

Zhang, Y., Zhou, B., Cai, X., Guo, W., Ding, X., & Yuan, X. (2021). Missing value imputation in multivariate time series with end-to-end generative adversarial networks. Information Sciences, 551, 67–82. https://doi.org/10.1016/j.ins.2020.11.035

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Published

2022-05-31

How to Cite

Mubarak, F. ., Aslanargun, A. ., & Sıklar, Ä°lyas . (2022). GSTARIMA Model with Missing Value for Forecasting Gold Price . Indonesian Journal of Statistics and Its Applications, 6(1), 90–100. https://doi.org/10.29244/ijsa.v6i1p90-100

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