Multivariate Time Series Imaging for Short-Term Precipitation Forecasting Using Convolutional Neural Networks
Sun Arthur A. Ojeda, Geoffrey A. Solano, Elmer C. Peramo · 2020
In this work, we explore the use of Convolutional Neural Networks to forecast discretized rainfall intensity by transforming multivariate time series data into image representations using Recurrence Plots (RP). A Convolutional Neural Network (CNN) architecture which takes these image representations in order to produce classification forecasts is proposed. Experimental results in classifying DOST-PAGASA' `s color-coded rainfall advisory yield 96.27% 10-fold cross validation accuracy with a configuration of 24-hour lag and 6-hour forecast horizon.