Dublin Core The Dublin Core metadata element set is common to all Omeka records, including items, files, and collections. For more information see, http://dublincore.org/documents/dces/. Title A name given to the resource Reviews Review Faculty Publications- Reviews Dublin Core The Dublin Core metadata element set is common to all Omeka records, including items, files, and collections. For more information see, http://dublincore.org/documents/dces/. Title A name given to the resource Analysis of Statistical and Deep Learning Techniques for Temperature Forecasting Subject The topic of the resource ARIMA; auto regression; GRU; LSTM; RNN; XAI Description An account of the resource In the field of meteorology, temperature forecasting is a significant task as it has been a key factor in industrial, agricultural, renewable energy, and other sectors. High accuracy in temperature forecasting is needed for decision-making in advance. Since temperature varies over time and has been studied to have non-trivial long-range correlation, non-linear behavior, and seasonal variability, it is important to implement an appropriate methodology to forecast accurately. In this paper, we have reviewed the performance of statistical approaches such as AR and ARIMA with RNN, LSTM, GRU, and LSTM-RNN Deep Learning models. The models were tested for short-term temperature forecasting for a period of 48 hours. Among the statistical models, the AR model showed notable performance with a r2 score of 0.955 for triennial 1 and for the same, the Deep Learning models also performed nearly equal to that of the statistical models and thus hybrid LSTM-RNN model was tested. The hybrid model obtained the highest r2 score of 0.960. The difference in RMSE, MAE and r2 scores are not significantly different for both Statistical and Vanilla Deep Learning approaches. However, the hybrid model provided a better r2 score, and LIME explanations have been generated for the same in order to understand the dependencies over a point forecast. Based on the reviewed results, it can be concluded that for short-term forecasting, both Statistical and Deep Learning models perform nearly equally. 2024 Bentham Science Publishers. Creator An entity primarily responsible for making the resource Kruthika S.G.; Rajasekaran U.; Alagarsamy M.; Sharma V. Source A related resource from which the described resource is derived Recent Advances in Computer Science and Communications, Vol-17, No. 2, pp. 49-65. Publisher An entity responsible for making the resource available Bentham Science Publishers Date A point or period of time associated with an event in the lifecycle of the resource 2024-01-01 Identifier An unambiguous reference to the resource within a given context <a href="https://doi.org/10.2174/0126662558264870231122113715" target="_blank" rel="noreferrer noopener">https://doi.org/10.2174/0126662558264870231122113715</a> <br /><br /><a href="https://www.scopus.com/inward/record.uri?eid=2-s2.0-85192173661&amp;doi=10.2174%2F0126662558264870231122113715&amp;partnerID=40&amp;md5=68f97185fdf806c7d78bd6e52b43a56a" target="_blank" rel="noreferrer noopener">https://www.scopus.com/inward/record.uri?eid=2-s2.0-85192173661&amp;doi=10.2174%2f0126662558264870231122113715&amp;partnerID=40&amp;md5=68f97185fdf806c7d78bd6e52b43a56a</a> Rights Information about rights held in and over the resource Restricted Access Relation A related resource ISSN: 26662558 Format The file format, physical medium, or dimensions of the resource Online Language A language of the resource English Type The nature or genre of the resource Review Coverage The spatial or temporal topic of the resource, the spatial applicability of the resource, or the jurisdiction under which the resource is relevant Kruthika S.G., Department of Computer Science and Business Systems, Thiagarajar College of Engineering, Tamil Nadu, Madurai, India; Rajasekaran U., Department of Computer Science and Business Systems, Thiagarajar College of Engineering, Tamil Nadu, Madurai, India; Alagarsamy M., Department of Computer Science and Business Systems, Thiagarajar College of Engineering, Tamil Nadu, Madurai, India; Sharma V., Department of Computational Sciences, CHRIST (Deemed to be University), NCR, Delhi, India