Forecasting of Meteorological Weather Time Series Through a Feature Vector Based on Correlation

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Canales F.G.
Chicoma C.G.
Elias D.A.
Garcia J.G.
Morales M.
Villota E.R.
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Springer Verlag
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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). © 2019, Springer Nature Switzerland AG.
Palabras clave
Weather forecast, Correlation, Deep Learning, Feature vector, Forecasting of time series, Non-linear forecast models