Abstract
The purpose of this study is to propose a new hybrid of space-time models by combining the time series regression (TSR), multivariate generalized space-time autoregressive (MGSTAR), and machine learning (ML) to forecast air pollution data in the city of Surabaya. The TSR model is used to capture linear patterns of data, especially trends and double seasonal. The MGSTAR model is employed to capture the relationship between locations, and the ML model is used to capture nonlinear patterns from the data. There are three ML models used in this study, namely feed-forward neural network (FFNN), deep learning neural network (DLNN), and long short-term memory (LSTM). So that there are three hybrid models used in this study, namely TSR-MGSTAR-FFNN, TSR-MGSTAR-DLNN, and TSR-MGSTAR-LSTM. The hybrid models will be used to forecast air pollution data consisting of CO, PM10, and NO2 at three locations in Surabaya simultaneously. Then, the performance of these three-combined hybrid models will be compared with the individual model of TSR and MGSTAR, two-combined hybrid models of MGSTAR-FFNN, MGSTAR-DLNN, MGSTAR-LSTM, and hybrid TSR-MGSTAR models based on the RMSE and sMAPE values in the out-of-sample data. Based on the smallest RMSE and sMAPE values, the analysis results show that the best model for forecasting CO is MGSTAR, forecasting PM10 is hybrid TSR-MGSTAR, and forecasting NO2 is hybrid TSR-MGSTAR-FFNN. In general, the hybrid model has better accuracy than the individual models. This result is in line with the results of the M3 and M4 forecasting competition.
This is a preview of subscription content, log in via an institution to check access.
Access this chapter
Tax calculation will be finalised at checkout
Purchases are for personal use only
Similar content being viewed by others
References
Chen, S., Zhang, D.: Impact of air pollution on labor productivity: evidence from prison factory data. China Econ. Q. Int. 1, 148–159 (2021)
Liu, Y., Zhou, Y., Lu, J.: Exploring the relationship between air pollution and meteorological conditions in china under environmental governance. Sci. Rep. 10, 14518 (2020)
Reames, T.G., Bravo, M.A.: People, place and pollution: investigating relationships between air quality perceptions, health concerns, exposure, and individual and area-level characteristics. Environ. Int. 122, 244–255 (2019)
Pfeifer, P.E., Deutsch, S.J.: A three stage iterative procedure for space-time modeling. Technometrics 22(1), 35–47 (1980)
Borovkova, S., Lopuhaa, R., Ruchjana, B.N.: Generalized STAR model with experimental weights. In: Proceedings of the 17th International Workshop on Statistical Modelling, pp. 143–151 (2002)
Suhartono, Nahdliyah, N., Akbar, M.S., Salehah, N.A., Choiruddin, A.: A MGSTAR: an extension of the generalized space-time autoregressive model. J. Phys. Conf. Ser. 1752, 012015 (2021)
Zhang, G.P.: Time series forecasting using a hybrid ARIMA and neural network model. Neurocomputing 50, 159–175 (2003)
LeCun, Y., Bengio, Y., Hinton, G.: Deep learning. Nature 521, 436–444 (2015)
Srivastava, S., Lessmann, S.: A comparative study of LSTM neural networks in forecasting day-ahead global horizontal irradiance with satellite data. Sol. Energy 162, 232–247 (2018)
Abbasimehr, H., Shabani, M., Yousefi, M.: An optimized model using LSTM network for demand forecasting. Comput. Ind. Eng. 143, 106435 (2020)
Makridakis, S., Spiliotis, E., Assimakopoulus, V.: The M4 competition: results, findings, conclusion and way forward. Int. J. Forecast. 34, 802–808 (2018)
Suhartono, Dana, I.M.G.M., Rahayu, S.P.: Hybrid model for forecasting space-time data with calendar variation effects. Telkomnika 17(1), 118–130 (2019)
Pusporani, E., Suhartono, Prastyo, D.D.: Hybrid multivariate generalized space-time autoregressive artificial neural network models to forecast air pollution data at Surabaya. In: AIP Conference Proceedings, vol. 2194, p. 020090 (2019)
Laily, V.O.N., Suhartono, Pusporani, E., Atok, R.M.: A novel hybrid Mgstar-Rnn model for forecasting spatio-temporal data. J. Phys. Conf. Ser. 1752, 012011 (2021)
Prastyo, D.D., Nabila, F.S., Suhartono, Lee, M.H., Suhermi, N., Fam, S.F.: VAR and GSTAR-based feature selection in support vector regression for multivariate spatio-temporal forecasting. In: Yap, B., Mohamed, A., Berry, M. (eds.) Soft Computing in Data Science. SCDS 2018. Communications in Computer and Information Science, vol. 937, pp. 46–57. Springer, Singapore (2018). https://doi.org/10.1007/978-981-13-3441-2_4
Suhartono, Prastyo, D.D., Kuswanto, H., Lee, M.H.: Comparison between VAR, GSTAR, FFNN-VAR and FFNN-GSTAR models for forecasting oil production. MATEMATIKA 34(1), 103–111 (2018)
Suhartono, Prabowo, H., Fam, S.F.: A hybrid TSR and LSTM for forecasting NO2 and SO2 in Surabaya. In: Berry, M., Yap, B., Mohamed, A., Köppen, M. (eds.) Soft Computing in Data Science. SCDS 2019. Communications in Computer and Information Science, vol. 1100, pp. 107–120. Springer, Singapore (2019). https://doi.org/10.1007/978-981-15-0399-3_9
Shummway, R.H., Stoffer, D.S.: Time Series Analysis and Its Application with R Examples. Springer, Pittsburg (2006). https://doi.org/10.1007/0-387-36276-2
Fausset, L.: Fundamental of Neural Network: Architectures Algorithms and Applications. Prentice-Hall Inc., Hoboken (1994)
Hochreiter, S., Schmiduber, J.: Long short-term memory. Neural Comput. 9(8), 1735–1780 (1997)
Ma, X., Tao, Z., Wang, Y., Yu, H., Wang, Y.: Long short-term memory neural network for traffic speed prediction using remote microwave sensor data. Transp. Res. Part C: Emerg. Technol. 54, 187–197 (2015)
Hajirahimi, Z., Khashei, M.: Hybrid structures in time series modeling and forecasting: a review. Eng. Appl. Artif. Intell. 86, 83–106 (2019)
Khashei, M., Bijari, M.: A novel hybridization of artificial neural networks and ARIMA models for time series forecasting. Appl. Soft Comput. 11, 2664–2675 (2011)
Wei, W.W.S.: Time Series Analysis Univariate and Multivariate Methods, 2nd edn. Pearson Education Inc., Boston (2006)
Suhartono, Suhermi, N., Prastyo, D.D.: Design of experiment to optimize the architecture of deep learning for nonlinear time series forecasting. Procedia Comput. Sci. 144, 269–276 (2018)
Makridakis, S., Hibbon, M.: The M3-competition result, conclusions and implications. Int. J. Forecast. 16, 451–676 (2000)
Acknowledgements
This research was supported by Deputi Bidang Penguatan Riset dan Pengembangan, Kementerian Riset dan Teknologi/ Badan Riset dan Inovasi Nasional under the scheme Penelitian Dasar, project no 3/E1/KP.PTNBH/2021 and 799/PKS/ITS/2021. The authors thank to DRPM ITS for the supports and to anonymous referees for their useful suggestions.
Author information
Authors and Affiliations
Corresponding author
Editor information
Editors and Affiliations
Rights and permissions
Copyright information
© 2021 Springer Nature Singapore Pte Ltd.
About this paper
Cite this paper
Prabowo, H., Prastyo, D.D., Setiawan (2021). Hybrid of Time Series Regression, Multivariate Generalized Space-Time Autoregressive, and Machine Learning for Forecasting Air Pollution. In: Mohamed, A., Yap, B.W., Zain, J.M., Berry, M.W. (eds) Soft Computing in Data Science. SCDS 2021. Communications in Computer and Information Science, vol 1489. Springer, Singapore. https://doi.org/10.1007/978-981-16-7334-4_26
Download citation
DOI: https://doi.org/10.1007/978-981-16-7334-4_26
Published:
Publisher Name: Springer, Singapore
Print ISBN: 978-981-16-7333-7
Online ISBN: 978-981-16-7334-4
eBook Packages: Computer ScienceComputer Science (R0)