Abstract
Nowadays, the impacts of climate change are harming many countries around the world. For this reason, the scientific community is interested in improving methods to forecast weather events, so it is possible to avoid people from being injured. One important thing in the development of time series forecasting methods is to consider the set of values over time that facilitates the prediction of future value. In this sense, we propose a new feature vector based on the correlation and autocorrelation functions. These measures reflect how the observations of a time series are related to each other. Then, univariate forecasting is performed using Multilayer Perceptron (MLP) and Long Short-Term Memory (LSTM) deep neural network. Finally, we compared the new model with linear and non-linear models. Reported results exhibit that MLP and LSTM models using the proposed feature vector, they show promising results for univariate forecasting. We tested our method on a real-world dataset from the Fisher weather station (Harvard Forest).
Access this chapter
Tax calculation will be finalised at checkout
Purchases are for personal use only
Similar content being viewed by others
References
Adhikari, R., Agrawal, R.K.: An introductory study on time series modeling and forecasting (2013). https://doi.org/10.13140/2.1.2771.8084
Alavi, S.E., Sinaei, H., Afsharirad, E.: Predict the trend of stock prices using machine learning techniques. Int. Acad. J. Econ. 2(12), 1–11 (2015)
Aminikhanghahi, S., Cook, D.J.: A survey of methods for time series change point detection. Knowl. Inf. Syst. 51, 1–29 (2016). https://doi.org/10.1007/s10115-016-0987-z
Bengio, Y., Courville, A.C., Vincent, P.: Unsupervised feature learning and deep learning: a review and new perspectives. CoRR, abs/1206.5538 1 (2012)
Boose, E.: Fisher meteorological station (since 2001). Harvard Forest Data Archive: HF001 (2001). https://doi.org/10.6073/pasta/04076dfd30b286c6c29301b6345a63f5
Cheng, H., Tan, P.-N., Gao, J., Scripps, J.: Multistep-ahead time series prediction. In: Ng, W.-K., Kitsuregawa, M., Li, J., Chang, K. (eds.) PAKDD 2006. LNCS (LNAI), vol. 3918, pp. 765–774. Springer, Heidelberg (2006). https://doi.org/10.1007/11731139_89
Collobert, R., Weston, J., Bottou, L., Karlen, M., Kavukcuoglu, K., Kuksa, P.: Natural language processing (almost) from scratch. J. Mach. Learn. Res. 12(Aug), 2493–2537 (2011)
Deng, L., et al.: Recent advances in deep learning for speech research at microsoft. In: 2013 IEEE International Conference on Acoustics, Speech and Signal Processing, pp. 8604–8608. IEEE (2013)
Doucoure, B., Agbossou, K., Cardenas, A.: Time series prediction using artificial wavelet neural network and multi-resolution analysis: application to wind speed data. Renew. Energy 92, 202–211 (2016). https://doi.org/10.1016/j.renene.2016.02.003. http://www.sciencedirect.com/science/article/pii/S0960148116301045
Erhan, D., Szegedy, C., Toshev, A., Anguelov, D.: Scalable object detection using deep neural networks. In: Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, pp. 2147–2154 (2014)
Hu, F., Li, L., Xu, X., Wang, J., Zhang, J.: Opinion extraction by distinguishing term dependencies and digging deep text features. NeuralComputing and Applications, February 2018. https://doi.org/10.1007/s00521-018-3372-x
Isasi Viñuela, P., Galván León, I.: Redes de neuronas artificiales. Un Enfoque Práctico, Editorial Pearson Educación SA Madrid España (2004)
Jozefowicz, R., Zaremba, W., Sutskever, I.: An empirical exploration of recurrent network architectures. In: Proceedings of the 32nd International Conference on International Conference on Machine Learning, ICML 2015, vol. 37, pp. 2342–2350, JMLR.org (2015). http://dl.acm.org/citation.cfm?id=3045118.3045367
Längkvist, M.: Modeling time-series with deep networks (2014)
Längkvist, M., Karlsson, L., Loutfi, A.: A review of unsupervised feature learning and deep learning for time-series modeling. Pattern Recogn. Lett. 42, 11–24 (2014)
LeCun, Y., Bengio, Y., Hinton, G.: Deep learning. Nature 521(7553), 436–444 (2015)
Lydia, M., Kumar, S.S., Selvakumar, A.I., Kumar, G.E.P.: Linear and non-linear autoregressive models for short-term wind speed forecasting. Energy Convers. Manag. 112, 115–124 (2016). https://doi.org/10.1016/j.enconman.2016.01.007. http://www.sciencedirect.com/science/article/pii/S0196890416000236
Mahalakshmi, G., Sridevi, S., Rajaram, S.: A survey on forecasting of time series data. In: 2016 International Conference on Computing Technologies and Intelligent Data Engineering, ICCTIDE 2016, pp. 1–8, January 2016. https://doi.org/10.1109/ICCTIDE.2016.7725358
Mauricio, J.A.: Introducción al análisis de series temporales. Universidad complutense de Madrid (2003)
Pascanu, R., Mikolov, T., Bengio, Y.: On the difficulty of training recurrent neural networks. ICML 3(28), 1310–1318 (2013)
Prakash, P., Pal, A.: Practical Time Series Analysis. Packt Publishing, Birmingham (2017)
Sapankevych, N.I., Sankar, R.: Time series prediction using support vector machines: a survey. IEEE Comput. Intell. Mag. 4(2), 24–38 (2009). https://doi.org/10.1109/MCI.2009.932254
Schulz, H., Behnke, S.: Deep learning. KI-Künstliche Intelligenz 26(4), 357–363 (2012)
Szegedy, C., Toshev, A., Erhan, D.: Deep neural networks for object detection. In: Advances in Neural Information Processing Systems, pp. 2553–2561 (2013)
Tsay, R.S.: Analysis of financial time series, vol. 543. Wiley, Hoboken (2005)
Acknowledgments
The authors would like to express their sincere gratitude to FONDECYT, which is an initiative of the National Council of Science, Technology and Technological Innovation (CONCYTEC), for promoting and financing collaborative research through the research circle N\(^{\circ }\) 148-2015-FONDECYT.
Author information
Authors and Affiliations
Corresponding author
Editor information
Editors and Affiliations
Rights and permissions
Copyright information
© 2019 Springer Nature Switzerland AG
About this paper
Cite this paper
Ramos, M.M.P., Del Alamo, C.L., Zapana, R.A. (2019). Forecasting of Meteorological Weather Time Series Through a Feature Vector Based on Correlation. In: Vento, M., Percannella, G. (eds) Computer Analysis of Images and Patterns. CAIP 2019. Lecture Notes in Computer Science(), vol 11678. Springer, Cham. https://doi.org/10.1007/978-3-030-29888-3_44
Download citation
DOI: https://doi.org/10.1007/978-3-030-29888-3_44
Published:
Publisher Name: Springer, Cham
Print ISBN: 978-3-030-29887-6
Online ISBN: 978-3-030-29888-3
eBook Packages: Computer ScienceComputer Science (R0)