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Multivariate Convolutional LSTMs for Relative Humidity Forecasting

The goal of predictive learning of temporal sequences is to forecast future values by learning from historical data. The accurate prediction of relative humidity using predictive models impacts the economic yield across various industries and sectors. Popular predictive models include recurrent neural networks. However, given the fact that relative humidity is not only dependant on its lag values, but rather, on other values such as dew point temperature, pressure and weather conditions in other areas, then such a predictive model can be boosted using a spatiotemporal grid with multiple variables in each cell. This paper presents a convolutional LSTM predictive model to learn complex multivariate and spatiotemporal correlation from a pseudo-image of meteorological variables taken from an area with multiple weather stations. Our model achieves superior performance compared to univariate and multivariate approaches. We show that using our method can predict far ahead into the future with competitive accuracy. Our method predicts relative humidity 10% better than univariate approaches, and outperforms current state-of-the-art methods by an average of 11% on test metrics.