Please use this identifier to cite or link to this item: http://hdl.handle.net/2080/3831
Title: A Comparative Analysis of Univariate Deep Learning-based Time-series Models for Temperature Forecasting of the Bhubaneshwar
Authors: Sabat, Naba Krushna
Nayak, Rashmiranjan
Pati, Umesh Chandra
Das, Santos Kumar
Keywords: Weather Prediction
LSTM and BiLSTM Models
N-Beats Model
GRU and BiGRU Models
Deep Learning Method
Issue Date: Dec-2022
Citation: IEEE International Symposium on Sustainable Energy, Signal Processing and Cyber Security (iSSSC-2022) ,GIET University, Gunupur, India, 15-17 December 2022
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 2 score, concludes that the prediction of the BiGRU model is more accurate as compared to the other implemented models.
Description: Copyright belongs to proceeding publisher
URI: http://hdl.handle.net/2080/3831
Appears in Collections:Conference Papers

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