Time series prediction based on machine learning: A case study, temperature forecasting in Vietnam
217 viewsDOI:
https://doi.org/10.54939/1859-1043.j.mst.85.2023.152-162Keywords:
Machine learning; Temperature forecast; Deep learning; Time series; SARIMA; XGBoost.Abstract
In recent years, there has been a surge in interest in the subject of machine learning for prediction. In this study, a temperature dataset of Vietnam’s stations is examined in order to anticipate temperature. Several forecasting models are used to accomplish this goal. First, a traditional time-series forecasting approach such as Seasonal Autoregressive Integrated Moving Average is used (SARIMA). Then, more complex approaches such as XGBoost, Encoder-Decoder, and Prophet are used. The models' performances are compared using several accuracy assessment methods (e.g., Root Mean Squared Error (RMSE) and Mean Absolute Error (MAE)). The findings demonstrate the superiority of the deep learning approach over the other methods.
References
[1]. IFRC, "World Disaster Report 2020," International Federation of Red Cross and Red Crescent Societies, (2020).
[2]. N. L. Duc et. al., "Research and quantitative ranifall forecasting from HRM and GSM model products," Vietnam Journal of Hydrometerorology, vol. 592, pp. 17-27, (2010).
[3]. G. P. Z. a. D. M. Kline, "Quarterly Time-Series Forecasting With Neural Networks," IEEE Transactions on Neural Networks, vol. 18, pp. 1800-1814, (2007). DOI: https://doi.org/10.1109/TNN.2007.896859
[4]. S. M. P. Chopra, “Supply Chain Management”. Strategy, Planning & Operation, German: Gabler, (2007).
[5]. K. H. M. J. B. A. Ahmad Hasan Nury, "Comparative study of wavelet-ARIMA and wavelet-ANN models for temperature time series data in northeastern Bangladesh," Journal of King Saud University - Science, vol. 29, no. 1, pp. 47-61, (2017). DOI: https://doi.org/10.1016/j.jksus.2015.12.002
[6]. C. N. B. a. B. E. Reddy, "Predictive data mining on Average Global Temperature using variants of ARIMA models," in IEEE-International Conference On Advances In Engineering, Science And Management (ICAESM -2012), (2012).
[7]. X. L. W. Q. Y. Z. W. Z. C. T. H. Yang, "A network traffic forecasting method based on SA optimized ARIMA–BP neural network," Computer Networks, vol. 193, (2021). DOI: https://doi.org/10.1016/j.comnet.2021.108102
[8]. S. R. H. P. S. S. P. C. Tandon, "How can we predict the impact of the social media messages on the value of cryptocurrency? Insights from big data analytics," International Journal of Information Management Data Insights, vol. 1, no. 2, (2021). DOI: https://doi.org/10.1016/j.jjimei.2021.100035
[9]. R. L. Carey Goh, "Modeling and forecasting tourism demand for arrivals with stochastic nonstationary seasonality and intervention,," Tourism Management, vol. 22, no. 5, pp. 499-510, (2002). DOI: https://doi.org/10.1016/S0261-5177(02)00009-2
[10]. L. A. H. Billy M. Williams, "Modeling and Forecasting Vehicular Traffic Flow as a Seasonal ARIMA Process: Theoretical Basis and Empirical Results," Journal of Transportation Engineering, vol. 129, no. 6, pp. 664-672, (2003). DOI: https://doi.org/10.1061/(ASCE)0733-947X(2003)129:6(664)
[11]. C. Hamzaçebi, "Improving artificial neural networks’ performance in seasonal time series forecasting," Information Sciences, vol. 178, no. 23, pp. 4550-4559, (2008). DOI: https://doi.org/10.1016/j.ins.2008.07.024
[12]. T. Miyano and F. Girosi, "Forecasting Global Temperature Variations by Neural Networks," Cambridge Artificial Intelligence Laboratory: Cambridge, MA,, USA, (1994). DOI: https://doi.org/10.21236/ADA290081
[13]. H. Hippert, C. Pedreira and R. Souza, "Combining neural networks and ARIMA models for hourly temperature forecast," in The IEEE-INNS-ENNS International Joint Conference on Neural Networks, Como, Italy, (2000). DOI: https://doi.org/10.1109/IJCNN.2000.860807
[14]. I. Tasadduq, S. Rehman and Bubshait, "Application of neural networks for the prediction of hourly mean surface temperatures in Saudi Arabia," Renew. Energy, vol. 11, pp. 71-77, (2001).
[15]. Maqsood, M. Khan and A. Abraham, "An ensemble of neural networks for weather forecasting," Neural Computing Application, vol. 13, pp. 112-122, (2004). DOI: https://doi.org/10.1007/s00521-004-0413-4
[16]. B. Smith, R. McClendon and G. Hoogenboom, "Improving air temperature prediction with artificial neural networks," Internaiontal Journal Computing Intelligent , vol. 3, pp. 179-186, (2006).
[17]. K. Abhishek, M. Singh, S. Ghosh and A. Anand, "Weather Forecasting Model using Artificial Neural Network," Procedia Technology, pp. 311-318, (2012). DOI: https://doi.org/10.1016/j.protcy.2012.05.047
[18]. H. Mori and D. Kanaoka, "Application of support vector regression to temperature forecasting for short-term load forecasting," in Proceedings of the 2007 International Joint Conference on Neural Networks, Orlando, Fl, USA, (2017). DOI: https://doi.org/10.1109/IJCNN.2007.4371109
[19]. E. Ortiz-García, S. Salcedo-Sanz, C. Casanova-Mateo, A. Paniagua-Tineo and J. Portilla-Figueras, "Accurate local very short-term temperature prediction based on synoptic situation Support Vector Regression banks," Atmos. Res., (2012). DOI: https://doi.org/10.1016/j.atmosres.2011.10.013
[20]. R. Chevalier, G. Hoogenboom, R. McClendon and J. Paz, "Support vector regression with reduced training sets for air temperature prediction: A comparison with artificial neural networks," Neural Computing Application, vol. 20, pp. 151-159, (2010). DOI: https://doi.org/10.1007/s00521-010-0363-y
[21]. A. Paniagua-Tineo, S. Salcedo-Sanz, C. Casanova-Mateo, E. Ortiz-García, M. Cony and E. Hernández-Martín, "Prediction of daily maximum temperature using a support vector regression algorithm," Renew. Energy, vol. 36, pp. 3054-3060, (2011). DOI: https://doi.org/10.1016/j.renene.2011.03.030
[22]. I. Abubakar, H. Chiroma, A. Zeki and M. Uddin, "Utilising key climate element variability for the prediction of future climate change using a support vector machine model," Int. J. Glob. Warm., vol. 9, pp. 129-151, (2016). DOI: https://doi.org/10.1504/IJGW.2016.074952
[23]. Z. Z. H. Wai Yan Nyein Naing, "Forecasting of monthly temperature variations using random forests," Asian Research Publishing Network Journal of Engineering and Applied Sciences, vol. 10, no. 21, pp. 10109-10112, (2015).
[24]. P. Hewage, M. Trovati, E. Pereira and Behera, "A. Deep learning-based effective fine-grained weather forecasting," Pattern Analysis, pp. 1-24, (2020). DOI: https://doi.org/10.1007/s10044-020-00898-1
[25]. I. Roesch and T. Günther, "Visualization of Neural Network Predictions for Weather Forecasting," Comput. Graph. Forum, pp. 209-220, (2018). DOI: https://doi.org/10.1111/cgf.13453
[26]. S. J. a. L. B. Taylor, "Forecasting at Scale," The American Statistician, vol. 72, no. 1, pp. 37-45, (2018). DOI: https://doi.org/10.1080/00031305.2017.1380080
[27]. K.-P. L. C.-S. L. P.-T. C. Ping-Feng Pai, "Time series forecasting by a seasonal support vector regression model," Expert Systems with Applications, vol. 37, no. 6, pp. 4261-4265, (2010). DOI: https://doi.org/10.1016/j.eswa.2009.11.076
[28]. T. H. M. Jr, A. M. Pellegrini and R. H. Perlis, "Assessment of Time-Series Machine Learning Methods for Forecasting Hospital Discharge Volume," JAMA Network Open, (2018).
[29]. A. S. K. S. S. a. U. H. Sandhiya, "Forecasting Website Traffic Using Prophet Time Series Model," International Research Journal of Multidisciplinary Technovation, vol. 1, no. 2, pp. 1-8, (2019).
[30]. W. D. B. U. N. N. H. Christophorus Beneditto Aditya Satrio, "Time series analysis and forecasting of coronavirus disease in Indonesia using ARIMA model and PROPHET," Procedia Computer Science, vol. 179, pp. 524-532, (2021). DOI: https://doi.org/10.1016/j.procs.2021.01.036
[31]. G. A. Rob J Hyndman, “Forecasting: principles and practice”, OTexts, (2018).
[32]. Y. B. J. Q. J. W. P. Y. S. W. Mingju Gong, "Gradient boosting machine for predicting return temperature of district heating system: A case study for residential buildings in Tianjin," Journal of Building Engineering, vol. 27, (2020). DOI: https://doi.org/10.1016/j.jobe.2019.100950
