0959e765-35f8-4779-bd48-265c7e6f4afa beie director@blueeyesintelligence.org 10.35940/ijitee.H9936.13080724 Ly N, Duong N, and Dai N, "Khi hau va thuy van tinh Quang Binh", Science and Technics Publishing House Science and Technics Publishing House, 2013Vaswani, Ashish, et al. "Attention is all you need." Advances in neural information processing systems 30 (2017).H. Wu, J. Xu, J. Wang, and M. Long, "Autoformer: Decomposition Transformers with Auto-Correlation for Long-Term Series Forecasting," Adv. Neural Inf. Process. Syst., vol. 27, no. NeurIPS, pp. 22419-22430, 2021.10.1609/aaai.v35i12.17325Haoyi Zhou and Shanghang Zhang and Jieqi Peng and Shuai Zhang and Jianxin Li and Hui Xiong and Wancai Zhang, "Informer: Beyond Efficient Transformer for Long Sequence TimeSeries Forecasting," The Thirty-Fifth AAAI Conference on Artificial Intelligence, AAAI 2021, Virtual Conference, vol. 35, 11106-11115, 2021. https://doi.org/10.1609/aaai.v35i12.17325Montgomery, Douglas C., Elizabeth A. Peck, and G. Geoffrey Vining. Introduction to linear regression analysis. John Wiley & Sons, 2021.10.1023/A:1010933404324Breiman, Leo. "Random forests." Machine learning 45 (2001): 5-32. https://doi.org/10.1023/A:101093340432410.1214/aos/1013203451Friedman, Jerome H. "Greedy function approximation: a gradient boosting machine." Annals of statistics (2001): 1189-1232. https://doi.org/10.1214/aos/101320345110.1016/S0925-2312(01)00702-0Zhang, G. Peter. "Time series forecasting using a hybrid ARIMA and neural network model." Neurocomputing 50 (2003): 159-175. https://doi.org/10.1016/S0925-2312(01)00702-010.3390/engproc2021005042Ouyang, Zuokun, Philippe Ravier, and Meryem Jabloun. "STL decomposition of time series can benefit forecasting done by statistical methods but not by machine learning ones." Engineering Proceedings 5.1 (2021): 42. https://doi.org/10.3390/engproc2021005042Zhou, Tian, et al. "Fedformer: Frequency enhanced decomposed transformer for long-term series forecasting." International Conference on Machine Learning. PMLR, 2022.Liu, Shizhan, et al. "Pyraformer: Low-complexity pyramidal attention for long-range time series modeling and forecasting." International conference on learning representations. 2021.10.1007/s42979-020-00180-5Athiyarath, Srihari, Mousumi Paul, and Srivatsa Krishnaswamy. "A comparative study and analysis of time series forecasting techniques." SN Computer Science 1.3 (2020): 175. https://doi.org/10.1007/s42979-020-00180-510.1007/978-981-16-4807-6_39Mehtab, Sidra, and Jaydip Sen. "Analysis and forecasting of financial time series using CNN and LSTM-based deep learning models." Advances in Distributed Computing and Machine Learning: Proceedings of ICADCML 2021. Springer Singapore, 2022. https://doi.org/10.1007/978-981-16-4807-6_3910.1145/3377713.3377722Yamak, Peter T., Li Yujian, and Pius K. Gadosey. "A comparison between arima, lstm, and gru for time series forecasting." Proceedings of the 2019 2nd international conference on algorithms, computing and artificial intelligence. 2019. https://doi.org/10.1145/3377713.3377722Elsworth, Steven, and Stefan Güttel. "Time series forecasting using LSTM networks: A symbolic approach." arXiv preprint arXiv:2003.05672 (2020).10.1016/j.ijforecast.2019.07.001Salinas, David, et al. "DeepAR: Probabilistic forecasting with autoregressive recurrent networks." International Journal of Forecasting 36.3 (2020): 1181-1191. https://doi.org/10.1016/j.ijforecast.2019.07.00110.1609/aaai.v37i9.26317Zeng, Ailing, et al. "Are transformers effective for time series forecasting?." Proceedings of the AAAI conference on artificial intelligence. Vol. 37. No. 9. 2023.10.1002/rvr2.63Hieu, T. T., Chieu, T. Q., Quang, D. N., & Hieu, N. D. (2023). Water level prediction using deep learning models: A case study of the Kien Giang River, Quang Binh province. River, 1-12. https://doi.org/10.1002/rvr2.6310.35940/ijitee.A4245.119119Dakshin, D., Rupesh, V. R., & Kumar, S. P. (2019). Water Hazard Prediction using Machine Learning. In International Journal of Innovative Technology and Exploring Engineering (Vol. 9, Issue 1, pp. 1451-1457). https://doi.org/10.35940/ijitee.a4245.11911910.35940/ijeat.D8452.049420Singh, N., & Panda, S. P. (2020). Stimulating Deep Learning Network on Graphical Processing Unit To Predict Water Level. In International Journal of Engineering and Advanced Technology (Vol. 9, Issue 4, pp. 1222-1229). https://doi.org/10.35940/ijeat.d8452.04942010.35940/ijrte.B4115.079220Perunkolam, A., Baskaran, M. P., Tripathi, S., & Sahu, O. P. (2020). Smart Water Distribution System using Machine Learning and IoT. In International Journal of Recent Technology and Engineering (IJRTE) (Vol. 9, Issue 2, pp. 1189-1194). https://doi.org/10.35940/ijrte.b4115.079220