A Series of Models based on Long Short Time Memory for Temperature Prediction

Abstract

Given the increasingly serious climate problems that have been appearing around the world in recent years, as part of the climate research, finding ways of smart, accurate machine learning to carry out the temperature prediction is of great significance both for human activities and the earth's ecosystem. In this study, 3 kinds of the Long Short Time Memory (LSTM) models, which differs on the way how the data is input and output and the network structure, respectively Directly-Multiple-Output (DMO), Single-Step-Scrolling (SSS), and Convolutional Neural Networks Plus Long Short Time Memory (CNN+LSTM) are built and trained by Pytorch on the Jena Climate dataset to compare their predicted performance. The training loss of these 3 models are 0.0053, 0.1568 and 0. 0079; testing loss are 0.0048, 0.1764 and 0.0096; Mean Absolute Percentage Error (MAPE) are 3.42%,6.58% and 3.30%. The result turns out that CNN+LSTM is the best model in comprehensive consideration with the least convergence time, low loss and MAPE, followed by the DMO model with the least loss and low MAPE but longer convergence time. SSS performs worst with increasing high loss. In general, CNN+LSTM is suitable for temperature prediction while DMO is also good when short convergence time is not needed. SSS is not recommended for temperature prediction.