MS-CLSTM: A Novel Deep Learning Approach for Forecasting Atmospheric Temperature Indices at Intervals
Keywords:
forecasting, time series, temporal convolution, structured encoding, multi-stackingAbstract
Time series forecasting plays a vital role in advancing fields like finance, economics, meteorology, and stock market analysis. It enables the prediction of future values by examining patterns in historical data. Forecasting becomes a great deal with recent strides in accuracy propelled by various deep learning techniques. In this study, we introduced a multi-stacked ConvLSTM (MS-CLSTM) model designed for precise periodic forecasting of atmospheric temperature indices. The model's performance was evaluated against Long Short-Term Memory (LSTM) and Temporal Convolution Network (TCN) models across diverse historical input windows and target prediction scenarios. Assessment metrics such as Mean Square Error (MSE) and Mean Absolute Error (MAE) were employed to gauge accuracy, particularly in predicting twelve-hour periodic temperature projections using a three-day historical temperature window as input. Our findings revealed a substantial improvement in performance during validation, showcasing a 42% reduction in MSE and a 20% decrease in MAE compared to LSTM. Additionally, when compared to TCN, our proposed model exhibited a 12% decrease in MSE and a 5% drop in MAE. Notably, the model consistently demonstrated strong performance across various input window sizes, encompassing historical information ranging from two to five days, and in predicting varying target scenarios.
Downloads
References
Zhou, Haoyi, Shanghang Zhang, Jieqi Peng, Shuai Zhang, Jianxin Li, Hui Xiong, and Wancai Zhang, "Informer: Beyond efficient transformer for long sequence time-series forecasting," In Proceedings of the AAAI conference on artificial intelligence, vol. 35, no. 12, pp. 11106-11115. 2021.
Wang, Kun, Chenhan Xu, Yan Zhang, Song Guo, and Albert Y. Zomaya. "Robust big data analytics for electricity price forecasting in the smart grid." IEEE Transactions on Big Data 5, no. 1 (2017): 34-45.
Li, Audeliano Wolian, and Guilherme Sousa Bastos. "Stock market forecasting using deep learning and technical analysis: a systematic review." IEEE access 8 (2020): 185232-185242.
Lefkowitz, Brett, and Moran Bodas. "Threat perception of climate change & public preparedness for extreme weather-related events in Bermuda-A cross-sectional study." International journal of disaster risk reduction 97 (2023): 104030.
Bilal, Muhammad, Hyeok Kim, Muhammad Fayaz, and Pravin Pawar. "Comparative analysis of time series forecasting approaches for household electricity consumption prediction." arXiv preprint arXiv:2207.01019 (2022).
Sufian, Md Abu, and Jayasree Varadarajan. "Enhancing Prediction and Analysis of UK Road Traffic Accident Severity Using AI: Integration of Machine Learning, Econometric Techniques, and Time Series Forecasting in Public Health Research." arXiv preprint arXiv:2309.13483 (2023).
Mandrikova, Oksana, Nadezhda Fetisova, and Yuriy Polozov. "Hybrid model for time series of complex structure with ARIMA components." Mathematics 9, no. 10 (2021): 1122.
Al-Qazzaz, Redha Ali, and Suhad A. Yousif. "High performance time series models using auto autoregressive integrated moving average." Indones. J. Electr. Eng. Comput. Sci 27 (2022): 422-430.
Sethi, Jasleen Kaur, and Mamta Mittal. "Analysis of air quality using univariate and multivariate time series models." In 2020 10th International Conference on Cloud Computing, Data Science & Engineering (Confluence), pp. 823-827. IEEE, 2020.
Sagheer, Alaa, and Mostafa Kotb. "Time series forecasting of petroleum production using deep LSTM recurrent networks." Neurocomputing 323 (2019): 203-213.
Junior, Paulo Rotela, Fernando Luiz Riêra Salomon, and Edson de Oliveira Pamplona. "ARIMA: An applied time series forecasting model for the Bovespa stock index." Applied Mathematics 5, no. 21 (2014): 3383.
Lim, Bryan, and Stefan Zohren. "Time-series forecasting with deep learning: a survey." Philosophical Transactions of the Royal Society A 379, no. 2194 (2021): 20200209.
Akşehir, Zinnet Duygu, and Erdal Kiliç. "How to handle data imbalance and feature selection problems in CNN-based stock price forecasting." IEEE Access 10 (2022): 31297-31305.
Liu, Shuai, Hong Ji, and Morgan C. Wang. "Nonpooling convolutional neural network forecasting for seasonal time series with trends." IEEE transactions on neural networks and learning systems 31, no. 8 (2019): 2879-2888.
Bi, Jing, Xiang Zhang, Haitao Yuan, Jia Zhang, and MengChu Zhou. "A hybrid prediction method for realistic network traffic with temporal convolutional network and LSTM." IEEE Transactions on Automation Science and Engineering 19, no. 3 (2021): 1869-1879.
Quang, Daniel, and Xiaohui Xie. "DanQ: a hybrid convolutional and recurrent deep neural network for quantifying the function of DNA sequences." Nucleic acids research 44, no. 11 (2016): e107-e107.
Shi, Xingjian, Zhourong Chen, Hao Wang, Dit-Yan Yeung, Wai-Kin Wong, and Wang-chun Woo. "Convolutional LSTM network: A machine learning approach for precipitation nowcasting." Advances in neural information processing systems 28 (2015).
Graves, Alex. "Generating sequences with recurrent neural networks." arXiv preprint arXiv:1308.0850 (2013).
Hochreiter, Sepp, and Jürgen Schmidhuber. "Long short-term memory." Neural computation 9, no. 8 (1997): 1735-1780.
Bai, Shaojie, J. Zico Kolter, and Vladlen Koltun. "An empirical evaluation of generic convolutional and recurrent networks for sequence modeling." arXiv preprint arXiv:1803.01271 (2018).
Chen, Jungan, Danjiang Chen, and Gaoping Liu. "Using temporal convolution network for remaining useful lifetime prediction." Engineering Reports 3, no. 3 (2021): e12305.
Lea, Colin, Michael D. Flynn, Rene Vidal, Austin Reiter, and Gregory D. Hager. "Temporal convolutional networks for action segmentation and detection." In proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, pp. 156-165. 2017.
Long, Jonathan, Evan Shelhamer, and Trevor Darrell. "Fully convolutional networks for semantic segmentation." In Proceedings of the IEEE conference on computer vision and pattern recognition, pp. 3431-3440. 2015.
Yu, Fisher, and Vladlen Koltun. "Multi-scale context aggregation by dilated convolutions." arXiv preprint arXiv:1511.07122 (2015).
Duc, Tho Nguyen, Chanh Tran Minh, Tan Phan Xuan, and Eiji Kamioka. "Convolutional neural networks for continuous QoE prediction in video streaming services." IEEE Access 8 (2020): 116268-116278.
He, Kaiming, Xiangyu Zhang, Shaoqing Ren, and Jian Sun. "Deep residual learning for image recognition." In Proceedings of the IEEE conference on computer vision and pattern recognition, pp. 770-778. 2016.
Downloads
Published
How to Cite
Issue
Section
License
This work is licensed under a Creative Commons Attribution-ShareAlike 4.0 International License.
All papers should be submitted electronically. All submitted manuscripts must be original work that is not under submission at another journal or under consideration for publication in another form, such as a monograph or chapter of a book. Authors of submitted papers are obligated not to submit their paper for publication elsewhere until an editorial decision is rendered on their submission. Further, authors of accepted papers are prohibited from publishing the results in other publications that appear before the paper is published in the Journal unless they receive approval for doing so from the Editor-In-Chief.
IJISAE open access articles are licensed under a Creative Commons Attribution-ShareAlike 4.0 International License. This license lets the audience to give appropriate credit, provide a link to the license, and indicate if changes were made and if they remix, transform, or build upon the material, they must distribute contributions under the same license as the original.
