A Modular Java-Based Framework for Deploying Deep Learning Time-Series Forecasting Models via ONNX

Farid Morsidi

doi:10.24191/jaeds.v5i2.144

Authors

Farid Morsidi Computing Department, Faculty of Computing & Meta-Technology, Universiti Pendidikan Sultan Idris, Tanjong Malim, Perak, Malaysia

DOI:

https://doi.org/10.24191/jaeds.v5i2.144

Keywords:

Rainfall prediction, Long Short-Term Memory (LSTM), Deep learning, Time-series forecasting, Deep Java Library (DJL)

Abstract

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References

S. Chen et al., “Rainfall Forecasting in Sub-Sahara Africa-Ghana using LSTM Deep Learning Approach,” Int. J. Eng. Res. Technol., vol. 10, no. 3, pp. 464–470, 2021, [Online]. Available: www.ijert.org

P. Kanchan, “Rainfall Analysis and Forecasting Using Deep Learning Technique,” J. Informatics Electr. Electron. Eng., vol. 2, no. 2, pp. 1–11, 2021, doi: 10.54060/jieee/002.02.015.

A. S. M and S. M. J. Amali, “RAINFALL DETECTION USING DEEP LEARNING TECHNIQUE,” J. Sci. Technol. Res., vol. 1, no. 5, pp. 37–42, 2024, [Online]. Available: https://philpapers.org/rec/ARURDU

F. Morsidi, “Multi-Depot Dispatch Deployment Analysis on Classifying Preparedness Phase for Flood-Prone Coastal Demography in Sarawak,” J. ICT Educ., vol. 9, no. 2, pp. 175–190, Dec. 2022, doi: 10.37134/jictie.vol9.2.13.2022.

D. Sun, J. Wu, H. Huang, R. Wang, F. Liang, and H. Xinhua, “Prediction of Short-time rainfall based on deep learning,” Math. Probl. Eng., vol. 2021, 2021, doi: 10.1155/2021/6664413.

F. Morsidi and I. Y. Panessai, “Overview of the Integral Impact of MDVRP Routing Variables on Routing Heuristics,” Appl. Inf. Technol. Comput. Sci., vol. 4(1), no. 1, pp. 1723–1738, 2023, doi: 10.30880/aitcs.2023.04.01.105.

E. Salcedo, “Graph Learning-based Regional Heavy Rainfall Prediction Using Low-Cost Rain Gauges,” 2024, doi: 10.1109/LA-CCI62337.2024.10814868.

S. D. Latif et al., “Assessing rainfall prediction models: Exploring the advantages of machine learning and remote sensing approaches,” Alexandria Eng. J., vol. 82, no. May, pp. 16–25, 2023, doi: 10.1016/j.aej.2023.09.060.

B. M. Preethi, R. Gowtham, S. Aishvarya, S. Karthick, and D. G. Sabareesh, “Rainfall Prediction using Machine Learning and Deep Learning Algorithms,” Int. J. Recent Technol. Eng., vol. 10, no. 4, pp. 251–254, 2021, doi: 10.35940/ijrte.d6611.1110421.

A. Paszke et al., “PyTorch: An Imperative Style, High-Performance Deep Learning Library,” in Advances in Neural Information Processing Systems, 2019.

Y. Wang, P. Jia, Z. Shu, K. Liu, and A. Rashid, “Multidimensional precipitation index prediction based on C NN -LSTM hybrid framework,” 2025, doi: https://doi.org/10.48550/arXiv.2504.20442.

A. Poghosyan et al., “An enterprise time series forecasting system for cloud applications using transfer learning,” Sensors, vol. 21, no. 5, pp. 1–28, 2021, doi: 10.3390/s21051590.

P. Lahti, H. Põldoja, J. Lehtonen, S. Ventelä, I. Tuomola, and M. Väyrynen, “An Enterprise Time Series Forecasting System for Cloud Applications Using Transfer Learning,” Sensors, vol. 21, no. 5, p. 1590, 2021, doi: 10.3390/s21051590.

D. Endalie, G. Haile, and W. Taye, “Deep learning model for daily rainfall prediction: case study of Jimma, Ethiopia,” Water Supply, vol. 22, no. 3, pp. 3448–3461, 2022, doi: 10.2166/WS.2021.391.

A.-C. Akazan, V. R. Mbingui, G. L. R. N’guessan, and I. Karambal, “Localized Weather Prediction Using Kolmogorov-Arnold Network-Based Models and Deep RNNs,” pp. 1–17, 2025, [Online]. Available: http://arxiv.org/abs/2505.22686

P. Hess and N. Boers, “Deep Learning for Improving Numerical Weather Prediction of Heavy Rainfall,” J. Adv. Model. Earth Syst., vol. 14, no. 3, pp. 1–11, 2022, doi: 10.1029/2021MS002765.

M. Asif, M. M. Kuglitsch, I. Pelivan, and R. Albano, “Review and Intercomparison of Machine Learning Applications for Short-term Flood Forecasting,” Water Resour. Manag., pp. 1971–1991, 2025, doi: 10.1007/s11269-025-04093-x.

R. A. Adewoyin, P. Dueben, P. Watson, Y. He, and R. Dutta, “TRU-NET: a deep learning approach to high resolution prediction of rainfall,” Mach. Learn., vol. 110, no. 8, pp. 2035–2062, 2021, doi: 10.1007/s10994-021-06022-6.

J. Zhang and K. Feng, “Forecast the future in a timeseries data with Deep Java Library (DJL),” 2025. [Online]. Available: https://docs.djl.ai/master/extensions/timeseries/docs/forecast_with_M5_data.html

R. Taylor, “Machine Learning Techniques for Fish Breeding Decision Making,” 2023 Wellingt. Fac. Eng. Symp., pp. 1–12, 2023, [Online]. Available: https://ojs.victoria.ac.nz/wfes/article/view/8422

A. Rácz, D. Bajusz, and K. Héberger, “Effect of dataset size and train/test split ratios in qsar/qspr multiclass classification,” Molecules, vol. 26, no. 4, pp. 1–16, 2021, doi: 10.3390/molecules26041111.

F. M. Javed Mehedi Shamrat et al., “LungNet22: A Fine-Tuned Model for Multiclass Classification and Prediction of Lung Disease Using X-ray Images,” J. Pers. Med., vol. 12, no. 5, 2022, doi: 10.3390/jpm12050680.

A. Bhardwaj, “Time Series Forecasting with Recurrent Neural Networks : An In-depth Analysis Time Series Forecasting with Recurrent Neural Networks : An In-depth Analysis and Comparative Study,” EDU J. Int. Aff. Res. (EJIAR), ISS, vol. 2, no. 4, 2024, [Online]. Available: https://edupublications.com/index.php/ejiar/article/view/36