A Comparative Analysis of Univariate Deep Learning-based Time-series Models for Temperature Forecasting of the Bhubaneshwar

Abstract

Meteorological variables such as temperature, humidity, and pressure significantly impact living things. Because of the ambiguity and rapid climatic change in the environment, weather prediction with higher accuracy is essential. With the help of deep learning models, the prediction of weather parameters becomes easier and more accurate as compared to traditional methods. This paper investigates various deep learning models such as Long Short Term Memory (LSTM), Gated Recurrent Unit (GRU), Bidirectional Long Short Term Memory (BiLSTM), Bidirectional Gated Recurrent Unit (BiGRU), and Neural Basis Expansion Analysis for Time Series (NBEATS) for the prediction of the temperature of the city of Bhubaneswar. The comparative analysis of these developed models in terms of various performance metrics, such as MAE, MSE, RMSE, and R <sup xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">2</sup> score, concludes that the prediction of the BiGRU model is more accurate as compared to the other implemented models.