UPSI Digital Repository (UDRep)
Start | FAQ | About

Improved of forecasting sea surface temperature based on hybrid arima and support vector machines models

AVAILABLE Article 1420 Views
Back to search
Scan QR Code to share or view item link:

Digital Assets & Download Files
Login required to access this item.
General Information Cached
Title
Improved of forecasting sea surface temperature based on hybrid arima and support vector machines models
Type
Article
Main Author
Nurul Hila Zainuddin
Additional Authors
  • Nawi, Wan Imanul Aisyah Wan Mohamad
  • Lola, Muhamad Safiih
  • Zakariya, Razak
  • Abd Hamid, Abd Aziz K.
Subject
QE Geology
ISSN
2289-599X
Language Code
eng
Bibliographic Details
Place of Production
Tanjong Malim
Publisher
Fakulti Sains dan Matematik
Year of publication
2021
Degree Type
Others
Corporate Name
Universiti Pendidikan Sultan Idris

Abstract

Forecasting is a very effortful task owing to its features which simultaneously contain linear and nonlinear patterns. The Autoregressive Integrated Moving Average (ARIMA) model has been one the most widely utilized linear model in time series forecasting. Unfortunately, the ARIMA model cannot effortlessly handle nonlinear patterns alone. Thus, Support Vector Machine (SVM) model is introduced to solve nonlinear behavior in the datasets with high variance and uncertainty. The purposes of this study are twofold. First, to propose a hybrid ARIMA models using SVM. Secondly, to test the effectiveness of the proposed hybrid model using sea surface temperature (SST) data. Our investigation is based on two well-known real datasets, i.e., SST (modis) and in-situ SST (hycom). Statistical measurement such as MAE, MAPE, MSE, and RMSE are carried out to investigate the efficacy of the proposed models as compared to the previous ARIMA and SVMs models. The empirical results reveal that the proposed models produce lesser MAE, MAPE, MSE, and RMSE values in comparison to the single ARIMA and SVMs models. In additional, ARIMA-SVM are much better than compared to the existing models since the forecasting values are closer to the actual value. Therefore, we conclude that the presented models can be used to generate superior predicting values in time series forecasting with a way higher forecast precision.

References

Akita, R., Yoshihara, A., Matsubara, T., & Uehara, K. (2016). Deep learning for stock prediction using numerical and textual information. Paper presented at the 2016 IEEE/ACIS 15th International Conference on Computer and Information Science, ICIS 2016 - Proceedings, doi:10.1109/ICIS.2016.7550882 Retrieved from www.scopus.com

Al Wadi, S., Ismail, M. T., Alkhahazaleh, M. H., & Addul Karim, S. A. (2011). Selecting wavelet transforms model in forecasting financial time series data based on ARIMA model. Applied Mathematical Sciences, 5(5-8), 315-326. Retrieved from www.scopus.com

Alaka, H. A., Oyedele, L. O., Owolabi, H. A., Kumar, V., Ajayi, S. O., Akinade, O. O., & Bilal, M. (2018). Systematic review of bankruptcy prediction models: Towards a framework for tool selection. Expert Systems with Applications, 94, 164-184. doi:10.1016/j.eswa.2017.10.040

Bleck, R. (2002). An oceanic general circulation model framed in hybrid isopycnic-cartesian coordinates. Ocean Modelling, 4(1), 55-88. doi:10.1016/S1463-5003(01)00012-9

Burbidge, R., Trotter, M., Buxton, B., & Holden, S. (2001). Drug design by machine learning: Support vector machines for pharmaceutical data analysis. Computers and Chemistry, 26(1), 5-14. doi:10.1016/S0097-8485(01)00094-8

Checkley, M. S., Higón, D. A., & Alles, H. (2017). The hasty wisdom of the mob: How market sentiment predicts stock market behavior. Expert Systems with Applications, 77, 256-263. doi:10.1016/j.eswa.2017.01.029

Cornillon, P. -., Imam, W., & Matzner-Løber, E. (2008). Forecasting time series using principal component analysis with respect to instrumental variables. Computational Statistics and Data Analysis, 52(3), 1269-1280. doi:10.1016/j.csda.2007.06.017

Fattah, J., Ezzine, L., Aman, Z., El Moussami, H., & Lachhab, A. (2018). Forecasting of demand using ARIMA model. International Journal of Engineering Business Management, 10 doi:10.1177/1847979018808673

Hipel, K. W., & McLeod, A. I. (1994). Time series modelling of water resources and environmental systems. Time Series Modelling of Water Resources and Environmental Systems, Retrieved from www.scopus.com

Huang, C. -., & Tsai, C. -. (2009). A hybrid SOFM-SVR with a filter-based feature selection for stock market forecasting. Expert Systems with Applications, 36(2 PART 1), 1529-1539. doi:10.1016/j.eswa.2007.11.062

Huang, W., Nakamori, Y., & Wang, S. -. (2005). Forecasting stock market movement direction with support vector machine. Computers and Operations Research, 32(10), 2513-2522. doi:10.1016/j.cor.2004.03.016

Ibrahim, M. Z., Zailan, R., Ismail, M., & Lola, M. S. (2009). Forecasting and time series analysis of air pollutants in several area of malaysia. American Journal of Environmental Sciences, 5(5), 625-632. Retrieved from www.scopus.com

Ibrahim, M. Z., Zailan, R., Ismail, M., & Lola, M. S. (2010). Time-series analysis of pollutants in east coast peninsular malaysia. Journal of Sustainability Science and Management, 5(1), 57-65. Retrieved from www.scopus.com

Ibrahim, M. Z., Zailan, R., Ismail, M., & Lola, M. S. (2010). Time-series analysis of pollutants in east coast peninsular malaysia. Journal of Sustainability Science and Management, 5(1), 57-65. Retrieved from www.scopus.com

Kim, K. -. (2003). Financial time series forecasting using support vector machines. Neurocomputing, 55(1-2), 307-319. doi:10.1016/S0925-2312(03)00372-2

Lola, M. S., Zainuddin, N. H., Abdullah, M. T., Ponniah, V., Ramlee, M. N. A., Zakariya, R., . . . Khalili, I. (2018). Improving the performance of ann-arima models for predicting water quality in the offshore area of kuala terengganu, terengganu, malaysia. Journal of Sustainability Science and Management, 13(1), 27-37. Retrieved from www.scopus.com

Lola, M. S., Zainuddin, N. H., Ramlee, M. N. A., & Sofyan, H. (2017). Double bootstrap control chart for monitoring sukuk volatility at bursa malaysia. Jurnal Teknologi, 79(6), 149-157. doi:10.11113/jt.v79.10410

López Iturriaga, F. J., & Sanz, I. P. (2015). Bankruptcy visualization and prediction using neural networks: A study of U.S. commercial banks. Expert Systems with Applications, 42(6), 2857-2869. doi:10.1016/j.eswa.2014.11.025

Makridakis, S., Andersen, A., Carbone, R., Fildes, R., Hibon, M., Lewandowski, R., . . . Winkler, R. (1982). The accuracy of extrapolation (time series) methods: Results of a forecasting competition. Journal of Forecasting, 1(2), 111-153. doi:10.1002/for.3980010202

McKenzie, E. (1984). General exponential smoothing and the equivalent arma process. Journal of Forecasting, 3(3), 333-344. doi:10.1002/for.3980030312

Metzger, E. J., & Hurlburt, H. E. (2001). The nondeterminstic nature of kuroshio penetration and eddy shedding in the south china sea. Journal of Physical Oceanography, 31(7), 1712-1732. doi:10.1175/1520-0485(2001)031<1712:TNNOKP>2.0.CO;2

Ming, W., Bao, Y., Hu, Z., & Xiong, T. (2014). Multistep-ahead air passengers traffic prediction with hybrid ARIMA-SVMs models. The Scientific World Journal, 2014 doi:10.1155/2014/567246

Muhamad Safiih, L., Nurul Hila, Z., Mohd Noor Afiq, R., Muhamad Na'eim, A. R., & Mohd Tajuddin, A. (2017). Improvement of estimation based on small number of events per variable (EPV) using bootstrap logistics regression model. Malaysian Journal of Fundamental and Applied Sciences, 13(4), 693-704. Retrieved from www.scopus.com

Pai, P. -., & Lin, C. -. (2005). A hybrid ARIMA and support vector machines model in stock price forecasting. International Journal of Management Science, 3(3), 497-505. Retrieved from www.scopus.com

Shin, K. -., Lee, T. S., & Kim, H. -. (2005). An application of support vector machines in bankruptcy prediction model. Expert Systems with Applications, 28(1), 127-135. doi:10.1016/j.eswa.2004.08.009

Shynkevich, Y., McGinnity, T. M., Coleman, S. A., & Belatreche, A. (2016). Forecasting movements of health-care stock prices based on different categories of news articles using multiple kernel learning. Decision Support Systems, 85, 74-83. doi:10.1016/j.dss.2016.03.001

Sugumaran, V., Sabareesh, G. R., & Ramachandran, K. I. (2008). Fault diagnostics of roller bearing using kernel based neighborhood score multi-class support vector machine. Expert Systems with Applications, 34(4), 3090-3098. doi:10.1016/j.eswa.2007.06.029

Sundari, R., Hadibarata, T., Rubiyatno., Malik, F. A., & Aziz, M. (2013). Multiple linear regression (MLR) modeling of wastewater in urban region of southern malaysia. Journal of Sustainability Science and Management, 8(1), 93-102. Retrieved from www.scopus.com

Syerrina, Z., Naeim, A. R., Muhamad Safiih, L., & Nuredayu, Z. (2017). Explorative spatial analysis of coastal community incomes in setiu wetlands: Geographically weighted regression. International Journal of Applied Engineering Research, 12(18), 7392-7396. Retrieved from www.scopus.com

Vapnik, V. (1995). The Nature of Statistical Learning Theory, Retrieved from www.scopus.com

Weng, B., Ahmed, M. A., & Megahed, F. M. (2017). Stock market one-day ahead movement prediction using disparate data sources. Expert Systems with Applications, 79, 153-163. doi:10.1016/j.eswa.2017.02.041

Weng, B., Ahmed, M. A., & Megahed, F. M. (2017). Stock market one-day ahead movement prediction using disparate data sources. Expert Systems with Applications, 79, 153-163. doi:10.1016/j.eswa.2017.02.041

Zainuddin, N. H., Lola, M. S., Djauhari, M. A., Yusof, F., Ramlee, M. N. A., Deraman, A., . . . Abdullah, M. T. (2019). Improvement of time forecasting models using a novel hybridization of bootstrap and double bootstrap artificial neural networks. Applied Soft Computing Journal, 84 doi:10.1016/j.asoc.2019.105676

Zhang, G., Eddy Patuwo, B., & Y. Hu, M. (1998). Forecasting with artificial neural networks: The state of the art. International Journal of Forecasting, 14(1), 35-62. doi:10.1016/S0169-2070(97)00044-7

Zhang, G. P. (2003). Time series forecasting using a hybrid ARIMA and neural network model. Neurocomputing, 50, 159-175. doi:10.1016/S0925-2312(01)00702-0

Zhou, X., Pan, Z., Hu, G., Tang, S., & Zhao, C. (2018). Stock market prediction on high-frequency data using generative adversarial nets. Mathematical Problems in Engineering, 2018 doi:10.1155/2018/4907423

This material may be protected under Copyright Act which governs the making of photocopies or reproductions of copyrighted materials.
You may use the digitized material for private study, scholarship, or research.
Back to search
Installed and configured by Bahagian Automasi, Perpustakaan Tuanku Bainun, Universiti Pendidikan Sultan Idris
If you have enquiries, kindly contact us at pustakasys@upsi.edu.my or 016-3630263. Office hours only.