Time series forecasting of styrene price using a hybrid ARIMA and neural network model

Main Article Content

Ali Ebrahimi Ghahnavieh


Every player in the market has a greater need to know about the smallest change in the market. Therefore, the ability to see what is ahead is a valuable advantage. The purpose of this research is to make an attempt to understand the behavioral patterns and try to find a new hybrid forecasting approach based on ARIMA-ANN for estimating styrene price. The time series analysis and forecasting is an essential tool which could be widely useful for finding the significant characteristics for making future decisions. In this study ARIMA, ANN and Hybrid ARIMA-ANN models were applied to evaluate the previous behavior of a time series data, in order to make interpretations about its future behavior for styrene price. Experimental results with real data sets show that the combined model can be most suitable to improve forecasting accurateness rather than traditional time series forecasting methodologies. As a subset of the literature, the small number of studies have been done to realize the new forecasting methods for forecasting styrene price.


Download data is not yet available.


Metrics Loading ...

Article Details



AHMAD, H. A.; DOZIER, G. V.;. ROLAND, D. A. (2001) Egg price forecasting using neural networks. Journal of Applied Poultry Research, v. 10, p. 162-171. DOI: 10.1093/japr/10.2.162

AHMAD, W. K. A.; AHMAD, S. (2013) Arima Model and Exponential Smoothing Method: A Comparison. AIP Conference Proceedings, DOI: 10.1063/1.4801282

AS' AD, M. (2012) Finding the best ARIMA model to forecast daily peak electricity demand. Proceedings of the Fifth Annual ASEARC Conference - Looking to the future - Programme and Proceedings, 2 - 3 February, University of Wollongong.

BARI, S. H.; RAHMAN, M. T.; HUSSAIN, M. M.; RAY, S. (2015) Forecasting monthly precipitation in Sylhet city using ARIMA model. Civil and Environmental Research, v. 7, n. 1, p. 69-77.

CHAKRAVARTY, A. (2016) Cognitive Routing - putting neurons in router.

Available at: https://www.linkedin.com/pulse/cognitive-routing-putting-neurons-router-amitabha-chakravarty/

CIECIERSKA, E.; BOCZKOWKA, A.; KUBIŚ, M.; CHABERA, P.; WIŚNIEWSKI, T. (2015) Effect of styrene addition on thermal properties of epoxy resin doped with carbon nanotubes. Polymers for Advanced Technologies, v. 26, n. 12, p. 1593-1599. DOI: 10.1002/pat.3586.

DAREKAR, A. S.; POKHARKAR, V. G.; DATARKAR, S. B. (2016) Onion Price Forecasting In Kolhapur Market of Western Maharashtra Using ARIMA Technique. International Journal of Information Research and Review, v. 3, n. 12, p. 3364-3368.

DENTON, J. W. (1995) How good are neural networks for causal forecasting?. The Journal of Business Forecasting, v. 14, n. 2, p. 17.

ENERGY, U. D. O. (2013) wiki/Styrene#cite_note-DOE2010-5. [Online]

Available at: http://www.enare,wikipedia.org.

GHAFFARI, A.; ZARE. S. (2009) A novel algorithm for prediction of crude oil price variation based on soft computing. Energy Economics, v. 31, n. 4, p. 531-536. DOI: 10.1016/j.eneco.2009.01.006.

GODARZI, A. A.; AMIRI, R. M.; TALAEI, A.; JAMASB, T. (2014) Predicting oil price movements: A dynamic Artificial Neural Network approach. Energy Policy, v. 68, p. 371-382. DOI: 10.1016/j.enpol.2013.12.049.

HAMID, S. A. (2004) Primer On using Neural Networks for Forecasting Market Variables. Southern New Hamshire University, s.n.

ICIS (2015) Styrene US Margin Report, London: Reed Business Information.

JAIN, G.;. MALLICK, B. (2017) A study of time series models ARIMA and ETS. Modern Education and Computer Science, v. 4, p. 57-63. DOI: 10.2139/ssrn.2898968.

KHASHEI, M.; BIJARI, M. (2011) A novel hybridization of artificial neural networks and ARIMA models for time series forecasting. Applied Soft Computing, v. 11, n. 2, p. 2664-2675. DOI: 10.1016/j.asoc.2010.10.015.

Lim, C.; MCALEER, M. (2001) Forecasting Tourist Arrivals. Annals of Tourism Research, v. 28, n. 4, p. 965-977. DOI: 10.1016/S0160-7383(01)00006-8.

MADDEN, G.; TAN, J. (2007) Forecasting Telecommunications Data with Linear Models. Telecommunications Policy, v. 31, n. 1, p. 31-44.

MIYASHITA, K. (2012) Ethyl Benzene (EB) & Styrene Monomer (SM). Toyo Engineering Korea Ltd. Available at: http://www.toyo-eng.com/kr/en/business/petrochemical/eb_sm/

MOMBEINI, H.; YAZDANI-CHAMZINI, A. (2015) Modeling gold price via artificial neural network. Journal of Economics, business and Management, v. 3, n. 7, p. 699-703.

MOVAGHARNEJAD, K.; MEHDIZADEH, B.; BANIHASHEMI, M.; KORDKHEILI, M. S. (2011) Forecasting the differences between various commercial oil prices in the Persian Gulf region by neural network. Energy, v. 36, n. 7, p. 3979-3984.

NAVEENA, K.; SUBEDAR, S.; SANTOSHA, R.; ABHISHEK, S. (2017) Hybrid time series modelling for forecasting the price of washed coffee (Arabica Plantation Coffee) in India. International Journal of Agriculture Sciences, v. 9, n. 10, p. 4004-4007.

NEWAZ, M. K. (2008) Comparing the Performance of Time Series Models for Forecasting Exchange Rate. BRAC University, p. 55-65.

OZOZEN, A.; KAYAKUTLU, G.; KETTERER, M.; KAYALICA, O. (2016) A combined seasonal ARIMA and ANN model for improved results in electricity spot price forecasting: Case study in Turkey. 2016 Portland International Conference on Management of Engineering and Technology (PICMET). DOI: 10.1109/PICMET.2016.7806831.

PANELLA, M.; BARCELLONA, F.; D’ECCLESIA, R. L. (2012) Forecasting energy commodity prices using neural networks. Advances in Decision Sciences, v. 2012. DOI: 10.1155/2012/289810.

PAO, H. T. (2007) Forecasting electricity market pricing using artificial neural networks. Energy Conversion and Management, v. 48, n. 3, p. 907-912.

RATHNAYAKA, R. M. K. T.; SENEVIRATNA, D. M. K. N.; JIANGUO, W.; ARUMAWADU, H. I. (2015) A hybrid statistical approach for stock market forecasting based on Artificial Neural Network and ARIMA time series models. 2015 International Conference on Behavioral, Economic and Socio-cultural Computing (BESC). DOI: 10.1109/BESC.2015.7365958.

RUSIMAN, M. S.; HAU, O. C.; ABDULLAH, A. W.; SUFAHANI, S. F.; AZMI, N. A. (2017) An Analysis of Time Series for the Prediction of Barramundi (Ikan Siakap) Price in Malaysia. Far East Journal of Mathematical Sciences, v. 102, n. 9, p. 2081-2093.

SHABRI, A.; SAMSUDIN, R. (2014) Daily crude oil price forecasting using hybridizing wavelet and artificial neural network model. Mathematical Problems in Engineering, v. 2014. DOI: 10.1155/2014/201402.

SHERMAN, L. M. (2010) Resin Prices Mostly Flat or Soft. Plastics Technology, Plastics Technology Magazine, v. 56, n. 11, p. 37-38.

TULARAM, G. A.; SAEED, T. (2016) Oil-price forecasting based on various univariate time-series models. American Journal of Operations Research, v. 6, n. 3, p. 226-235.

VEIGA, C. P.; VEIGA, C. R. P.; CATAPAN, A.; TORTATO, U.; SILVA, W. V. (2014) Demand Forecasting in Food Retail: A Comparison between the Holt-Winters and ARIMA Models. WSEAS Transactions on Business and Economics, v. 11, p. 608-614.

YADAV, A.; SAHU, K. (2017) Wind forecasting using artificial neural networks: a survey and taxonomy. International Journal of Research In Science & Engineering, v. 3, n. 2, p. 148-155.

YOUSEFI, M.; HOOSHYAR, D.; YOUSEFI, M.; KHAKSAR. W.; SAHARI, K. S. M.; ALNAIMI, F. B. I. (2015) An artificial neural network hybrid with wavelet transform for short-term wind speed forecasting: A preliminary case study. 2015 International Conference on Science in Information Technology (ICSITech). DOI: 10.1109/ICSITech.2015.7407784.

YOUSIF, E.; HADDAD, R. (2013) Photodegradation and photostabilization of polymers, especially polystyrene. SpringerPlus, v. 2, n. 1, p. 398.

YU, L.; WANG, S.; LAI, K. K. (2008) Forecasting crude oil price with an EMD-based neural network ensemble learning paradigm. Energy Economics, v. 30, n. 5, p. 2623-2635.

ZHANG, G. P. (2003) Time series forecasting using a hybrid ARIMA and neural network model. Neurocomputing, v. 50, p. 159-175.

Similar Articles

You may also start an advanced similarity search for this article.