The implementation of the ARIMA-ARCH model using data mining for forecasting rainfall in Bandung city

Monika, P.; Ruchjana, B.; Abdulla, A.

Growing Science » International Journal of Data and Network Science » The implementation of the ARIMA-ARCH model using data mining for forecasting rainfall in Bandung city

Journals

IJDS Volumes

Volume 1 (8)
- Issue 1 (5)
- Issue 2 (3)

Volume 2 (12)
- Issue 1 (3)
- Issue 2 (3)
- Issue 3 (3)
- Issue 4 (3)

Volume 3 (27)
- Issue 1 (4)
- Issue 2 (9)
- Issue 3 (8)
- Issue 4 (6)

Volume 4 (37)
- Issue 1 (6)
- Issue 2 (15)
- Issue 3 (7)
- Issue 4 (9)

Volume 5 (86)
- Issue 1 (9)
- Issue 2 (11)
- Issue 3 (32)
- Issue 4 (34)

Volume 6 (163)
- Issue 1 (30)
- Issue 2 (33)
- Issue 3 (40)
- Issue 4 (60)

Volume 7 (200)
- Issue 1 (53)
- Issue 2 (46)
- Issue 3 (46)
- Issue 4 (55)

Volume 8 (243)
- Issue 1 (60)
- Issue 2 (61)
- Issue 3 (60)
- Issue 4 (62)

Countries

International Journal of Data and Network Science

ISSN 2561-8156 (Online) - ISSN 2561-8148 (Print) Quarterly Publication Volume 6 Issue 4 pp. 1309-1318 , 2022

The implementation of the ARIMA-ARCH model using data mining for forecasting rainfall in Bandung city Pages 1309-1318 Download PDF

Authors: Putri Monika, Budi Nurani Ruchjana, Atje Setiawan Abdulla

DOI: 10.5267/j.ijdns.2022.6.004

Keywords: ARIMA-ARCH, Data Mining, KDD, Forecasting, Rainfall

Abstract: A time series is a stochastic process which is arranged by time simultaneously. In this article, a time series model is used in accordance with Box-Jenkins' procedure. The Box-Jenkins procedure consists in identifying the model, estimating the parameters and diagnostic checking. The time series model is differentiated according to the number of variables, i.e. univariate and multivariate. The univariate method for the time series model that is often used is the Autoregressive Integrated Moving Average (ARIMA) model and the multivariate time series model is the Vector Autoregressive Integrated Moving Average (VARIMA) model. In this research, we studied the ARIMA model which is studied with a non-constant error variance. In this case, the Autoregressive Conditional Heteroscedasticity (ARCH) model is applied to outgrow the non-constant error variance. Selection of the best model by examining the minimum AIC for each model. The ARIMA-ARCH model is implemented on rainfall data in Bandung city with Knowledge Discovery in Database (KDD) in Data Mining. The methodology in the KDD process, including pre-processing, data mining process, and post-processing. Based on the results of model fitting, the best model is the ARIMA (2,1,4)-ARCH (1) model. The result of forecasting rainfall in Bandung shows a MAPE value is 11%, which has a similar pattern with actual data for short time 2-4 days. From these results, we conclude that the ARIMA-ARCH model is a good model for forecasting the rainfall in Bandung city.

How to cite this paper

 Monika, P., Ruchjana, B & Abdulla, A. (2022). The implementation of the ARIMA-ARCH model using data mining for forecasting rainfall in Bandung city.International Journal of Data and Network Science, 6(4), 1309-1318.

Refrences

Abhishek, K., Kumar, A., Ranjan, R., & Kumar, S. (2012, July). A rainfall prediction model using artificial neural net-work. In 2012 IEEE Control and System Graduate Research Colloquium (pp. 82-87). IEEE.
Agboola, A. H., Gabriel, A. J., Aliyu, E. O., & Alese, B. K. (2013). Development of a fuzzy logic based rainfall prediction model. International journal of Engineering and Technology, 3(4), 427-435.
Anuradha, J. (2015). A brief introduction on Big Data 5Vs characteristics and Hadoop technology. Procedia computer sci-ence, 48, 319-324.
BMKG (2022). https://www.bmkg.go.id/iklim/dinamika-atmosfir.bmkg. (Online: Accessed March 1, 2022).
Bollerslev, T. (1986). Generalized autoregressive conditional heteroskedasticity. Journal of econometrics, 31(3), 307-327.
Bonar, H., Ruchjana, B. N., & Darmawan, G. (2017, March). Development of generalized space time autoregressive inte-grated with ARCH error (GSTARI–ARCH) model based on consumer price index phenomenon at several cities in North Sumatera province. In AIP Conference Proceedings (Vol. 1827, No. 1, p. 020009). AIP Publishing LLC.
Box, G. E., & Pierce, D. A. (1970). Distribution of residual autocorrelations in autoregressive-integrated moving average time series models. Journal of the American statistical Association, 65(332), 1509-1526.
Box, G. E., & Jenkins, G. M. (1976). Time series analysis: Forecasting and control. San Francisco. Calif: Holden-Day.
Chau, K. W., & Wu, C. L. (2010). A hybrid model coupled with singular spectrum analysis for daily rainfall predic-tion. Journal of Hydroinformatics, 12(4), 458-473.
Engle, R. F. (1982). Autoregressive conditional heteroscedasticity with estimates of the variance of United Kingdom in-flation. Econometrica: Journal of the econometric society, 987-1007.
Han, J., Kamber, M., and Pei,J., (2012) 'Data Mining: Concept and Techniques'. Elsevier,Inc.
Hernández, E., Sanchez-Anguix, V., Julian, V., Palanca, J., & Duque, N. (2016, April). Rainfall prediction: A deep learn-ing approach. In International Conference on Hybrid Artificial Intelligence Systems (pp. 151-162). Springer, Cham.
Larose, D. T., & Larose, C. D. (2014). Discovering knowledge in data: an introduction to data mining (Vol. 4). John Wiley & Sons.
Lawrence, K. D., Klimberg, R. K., & Lawrence, S. M. (2009). Fundamentals of forecasting using excel. Industrial Press Inc.
Lee, S., Cho, S., & Wong, P. M. (1998). Rainfall prediction using artificial neural networks. journal of geographic infor-mation and Decision Analysis, 2(2), 233-242.
Ling, S., & McAleer, M. (2003). Asymptotic theory for a vector ARMA-GARCH model. Econometric theory, 19(2), 280-310.
Ljung, G. M. (1986). Diagnostic testing of univariate time series models. Biometrika, 73(3), 725-730.
Mishra, N., Soni, H. K., Sharma, S., & Upadhyay, A. K. (2018). Development and Analysis of Artificial Neural Network Models for Rainfall Prediction by Using Time-Series Data. International Journal of Intelligent Systems & Applica-tions, 10(1).
Nainggolan, N., Ruchjana, B. N., Darwis, S., & Siregar, R. E. (2010). GSTAR Models with ARCH Errors and The Simula-tion. In Proceeding of the Third Internastional Conference on Mathematics and Natural Sciences (ICMNS2010) (pp. 1075-1084).
Nayak, D. R., Mahapatra, A., & Mishra, P. (2013). A survey on rainfall prediction using artificial neural net-work. International Journal of Computer Applications, 72(16).
Nikam, V. B., & Meshram, B. B. (2013, September). Modeling rainfall prediction using data mining method: A Bayesian approach. In 2013 Fifth International Conference on Computational Intelligence, Modelling and Simulation, 132-136.
Power NASA (2022). Prediction of Worldwide Energy Resource. https://power.larc.nasa.gov/data-access-viewer/. (Online: Accessed March 1, 2022).
Sahai, A. K., Soman, M. K., & Satyan, V. (2000). All India summer monsoon rainfall prediction using an artificial neural network. Climate dynamics, 16(4), 291-302.
Sethi, N., & Garg, K. (2014). Exploiting data mining technique for rainfall prediction. International Journal of Computer Science and Information Technologies, 5(3), 3982-3984.
Toth, E., Brath, A., & Montanari, A. (2000). Comparison of short-term rainfall prediction models for real-time flood fore-casting. Journal of hydrology, 239(1-4), 132-147.
Venkata Ramana, R., Krishna, B., Kumar, S. R., & Pandey, N. G. (2013). Monthly rainfall prediction using wavelet neural network analysis. Water resources management, 27(10), 3697-3711.
Wei, W. (2006). Time Series Analysis: Univariate and Multivariate Methods (2nd Edition). In Pearson: Addison Wesley.
Wei, W. (2019). Multivariate Time Series Analysis and Applications. John Wiley & Son Ltd.
Wong, K. W., Wong, P. M., Gedeon, T. D., & Fung, C. C. (2003). Rainfall prediction model using soft computing tech-nique. Soft Computing, 7(6), 434-438.
Yan, B., & Chen, Z. (2018). A prediction approach for precise marketing based on ARIMA-ARCH Model: A case of China Mobile. Communications in Statistics-Theory and Methods, 47(16), 4042-4058.
Yusof, F., Kane, I. L., & Yusop, Z. (2013). Hybrid of ARIMA-GARCH modeling in rainfall time series. Jurnal Teknolo-gi, 63(2).
Zainudin, S., Jasim, D. S., & Bakar, A. A. (2016). Comparative analysis of data mining techniques for Malaysian rainfall prediction. International Journal of Advanced Science Engineering Information Technology, 6(6), 1148-1153.