Abstract
Machine learning is a booming technical term in every domain of research. The majority of the technical concepts sounds to accomplish classification task in a real-life scenario. In the literature, the huge number of classification tools, it becomes very necessary to justify the performance of machine learning classifiers. This paper describes four classification techniques that are successfully applied for the prediction of the two most significant features for weather forecasting temperature and relative humidity (RH). A brief introduction of the proposed model with four prediction methodologies—ARMA, MLP, SVM and ELANFIS—follows the discriminate ideas that can create the space for such research. The techniques are then compared on a public data set containing the time series of the two parameters: temperature and relative humidity. As per the data statistics, the parameters are registered on an hourly basis and recorded over a field in an Italian city. An elaborating analysis of the results is performed to provide insights into the satisfactory performance of the models.
Keywords
- Time series prediction
- Weather forecasting
- Support vector machine (SVM)
- Multi-layer perceptron (MLP)
This is a preview of subscription content, access via your institution.
Buying options
References
Adhikari, R., Agrawal, R.K.: An introductory study on time series modeling and forecasting (2013). arXiv:1302.6613
Ajil, K.S., Thapliyal, P.K., Shukla, M.V., Pal, P.K., Joshi, P.C., Navalgund, R.R.: A new technique for temperature and humidity profile retrieval from infrared-sounder observations using the adaptive neuro-fuzzy inference system. IEEE Trans. Geosci. Remote Sens. 48(4), 1650–1659 (2010)
Chang, C.C., Lin, C.J.: Libsvm: a library for support vector machines. ACM Trans. Intell. Syst. Technol. (TIST) 2(3), 1–27 (2011)
Daneshmand, H., Tavousi, T., Khosravi, M., Tavakoli, S.: Modeling minimum temperature using adaptive neuro-fuzzy inference system based on spectral analysis of climate indices: A case study in iran. J. Saudi Society Agric. Sci. 14(1), 33–40 (2015)
Dua, D., Graff, C., et al.: Uci machine learning repository (2017)
Fan, Y., Li, P., Song, Z.: Dynamic least squares support vector machine. In: 2006 6th World Congress on Intelligent Control and Automation, vol. 1, pp. 4886–4889. IEEE (2006)
Hamzaçebi, C.: Improving artificial neural networks-performance in seasonal time series forecasting. Inf. Sci. 178(23), 4550–4559 (2008)
Hipel, K.W., McLeod, A.I.: Time series modelling of water resources and environmental systems. Elsevier (1994)
Huang, G.B., Zhu, Q.Y., Siew, C.K.: Extreme learning machine: theory and applications. Neurocomputing 70(1–3), 489–501 (2006)
Jang, J.S.: Anfis: adaptive-network-based fuzzy inference system. IEEE Trans. Syst. Man Cybern. 23(3), 665–685 (1993)
Kumar, N.: Better performance in human action recognition from spatiotemporal depth information features classification. In: Computational Network Application Tools for Performance Management, pp. 39–51. Springer (2020)
Kumar, N.: Recent issues with machine vision applications for deep network architectures. In: Cognitive Computing Systems, pp. 267–284. Apple Academic Press (2021)
Li, G., Shi, J.: On comparing three artificial neural networks for wind speed forecasting. Appl. Energy 87(7), 2313–2320 (2010)
Martínez-Martínez, V., Baladrón, C., Gomez-Gil, J., Ruiz-Ruiz, G., Navas-Gracia, L.M., Aguiar, J.M., Carro, B.: Temperature and relative humidity estimation and prediction in the tobacco drying process using artificial neural networks. Sensors 12(10), 14004–14021 (2012)
Mohammadi, K., Shamshirband, S., Motamedi, S., Petković, D., Hashim, R., Gocic, M.: Extreme learning machine based prediction of daily dew point temperature. Comput. Electron. Agric. 117, 214–225 (2015)
Rojas, I., Valenzuela, O., Rojas, F., Guillén, A., Herrera, L.J., Pomares, H., Marquez, L., Pasadas, M.: Soft-computing techniques and ARMA model for time series prediction. Neurocomputing 71(4–6), 519–537 (2008)
Shamshirband, S., Mohammadi, K., Chen, H.L., Samy, G.N., Petković, D., Ma, C.: Daily global solar radiation prediction from air temperatures using kernel extreme learning machine: a case study for Iran. J. Atmos. Solar-Terrestrial Phys. 134, 109–117 (2015)
Suryono, S., Saputra, R., Surarso, B., Sukri, H.: Web-based fuzzy time series for environmental temperature and relative humidity prediction. In: 2017 IEEE International Conference on Communication, Networks and Satellite (Comnetsat), pp. 36–41. IEEE (2017)
Zhou, S., Chu, X., Cao, S., Liu, X., Zhou, Y.: Prediction of the ground temperature with ann, ls-svm and fuzzy ls-svm for gshp application. Geothermics 84, 101757 (2020)
Author information
Authors and Affiliations
Corresponding author
Editor information
Editors and Affiliations
Rights and permissions
Copyright information
© 2022 The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd.
About this paper
Cite this paper
Sharma, S., Bhatt, K.K., Chabra, R., Aneja, N. (2022). A Comparative Performance Model of Machine Learning Classifiers on Time Series Prediction for Weather Forecasting. In: Goar, V., Kuri, M., Kumar, R., Senjyu, T. (eds) Advances in Information Communication Technology and Computing. Lecture Notes in Networks and Systems, vol 392. Springer, Singapore. https://doi.org/10.1007/978-981-19-0619-0_50
Download citation
DOI: https://doi.org/10.1007/978-981-19-0619-0_50
Published:
Publisher Name: Springer, Singapore
Print ISBN: 978-981-19-0618-3
Online ISBN: 978-981-19-0619-0
eBook Packages: EngineeringEngineering (R0)