Please use this identifier to cite or link to this item: http://hdl.handle.net/2080/3831
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dc.contributor.authorSabat, Naba Krushna-
dc.contributor.authorNayak, Rashmiranjan-
dc.contributor.authorPati, Umesh Chandra-
dc.contributor.authorDas, Santos Kumar-
dc.date.accessioned2022-12-26T10:39:58Z-
dc.date.available2022-12-26T10:39:58Z-
dc.date.issued2022-12-
dc.identifier.citationIEEE International Symposium on Sustainable Energy, Signal Processing and Cyber Security (iSSSC-2022) ,GIET University, Gunupur, India, 15-17 December 2022en_US
dc.identifier.urihttp://hdl.handle.net/2080/3831-
dc.descriptionCopyright belongs to proceeding publisheren_US
dc.description.abstractMeteorological 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.en_US
dc.subjectWeather Predictionen_US
dc.subjectLSTM and BiLSTM Modelsen_US
dc.subjectN-Beats Modelen_US
dc.subjectGRU and BiGRU Modelsen_US
dc.subjectDeep Learning Methoden_US
dc.titleA Comparative Analysis of Univariate Deep Learning-based Time-series Models for Temperature Forecasting of the Bhubaneshwaren_US
dc.typeArticleen_US
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