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).
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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.
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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
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