This work is licensed under the Creative Commons Attribution-NonCommercial-NoDerivatives 4.0 International License.
Kotu, V., B. Deshpande. Time Series Forecasting. – Data Sci., 2019, pp. 395-445. DOI: 10.1016/B978-0-12-814761-0.00012-5.Search in Google Scholar
Schmidl, S., P. Wenig, T. Papenbrock. Anomaly Detection in Time Series: A Comprehensive Evaluation. – Proc. VLDB Endow., Vol. 15, 2022, No 9, pp. 1779-1797. DOI: 10.14778/3538598.3538602.Search in Google Scholar
Choi, K., J. Yi, C. Park, S. Yoon. Deep Learning for Anomaly Detection in Time-Series Data: Review, Analysis, and Guidelines. – IEEE Access, Vol. 9, 2021, pp. 120043-120065. DOI: 10.1109/ACCESS.2021.3107975.Search in Google Scholar
Pravin, P. S., J. Z. M. Tan, K. S. Yap, Z. Wu. Hyperparameter Optimization Strategies for Machine Learning-Based Stochastic Energy Efficient Scheduling in Cyber-Physical Production Systems. – Digit. Chem. Eng., Vol. 4, 2022, p. 100047. DOI: 10.1016/J.DCHE.2022.100047.Search in Google Scholar
Bergstra, J., Y. Bengio. Random Search for Hyper-Parameter Optimization. – J. Mach. Learn. Res., Vol. 13, 2012, No 10, pp. 281-305 (online). http://jmlr.org/papers/v13/bergstra12a.htmlSearch in Google Scholar
Bergstra, J., R. Bardenet, Y. Bengio, B. Kégl. Algorithms for Hyper-Parameter Optimization. – In: Proc. of Adv. Neural Inf. Process. Syst. 24: 25th Annu. Conf. Neural Inf. Process. Syst. 2011 (NIPS’11), 2011, pp. 1-9.Search in Google Scholar
Lim, B., S. Arik, N. Loeff, T. Pfister. Temporal Fusion Transformers for Interpretable Multi-Horizon Time Series Forecasting. – Int. J. Forecast., Vol. 37, 2021, No 4, pp. 1748-1764. DOI: 10.1016/j.ijforecast.2021.03.012.Search in Google Scholar
Liu, P., W. Ming, C. Huang. Intelligent Modeling of Abnormal Demand Forecasting for Medical Consumables in Smart City. – Environ. Technol. Innov., Vol. 20, 2020, p. 101069. DOI: 10.1016/j.eti.2020.101069.Search in Google Scholar
Kalifa, D., U. Singer, I. Guy, G. D. Rosin, K. Radinsky. Leveraging World Events to Predict e-Commerce Consumer Demand under Anomaly. – Association for Computing Machinery, Vol. 1, 2022, No 1. DOI: 10.1145/3488560.3498452.Search in Google Scholar
Sharma, K., R. Bhalla, G. Ganesan. Time Series Forecasting Using FB-Prophet. – CEUR Workshop Proc., Vol. 3445, 2022, pp. 59-65.Search in Google Scholar
El-Hadad, R., Y. F. Tan, W. N. Tan. Anomaly Prediction in Electricity Consumption Using a Combination of Machine Learning Techniques. – Int. J. Technol., Vol. 13, 2022, No 6, pp. 1317-1325. DOI: 10.14716/ijtech.v13i6.5931.Search in Google Scholar
Liu, F. T., K. M. Ting, Z. H. Zhou. Isolation Forest. – In: Proc. of IEEE Int. Conf. Data Mining, ICDM, 2008, pp. 413-422. DOI: 10.1109/ICDM.2008.17.Search in Google Scholar
Cutler, A., D. R. Cutler, J. R. Stevens. Random Forests. – In: C. Zhang, Y. Ma, Eds. Ensemble Machine Learning. New York, NY, Springer New York, 2012, pp. 157-175. DOI: 10.1007/978-1-4419-9326-7_5.Search in Google Scholar
Luochen, X., N. Hasachoo. The Study of Irregular Demand Forecasting for Medicines: The Case Study of ABC Medical Center Hospital. – In: Proc. of 10th Int. Conf. Ind. Technol. Manag. (ICITM’21), 2021, pp. 115-120. DOI: 10.1109/ICITM52822.2021.00028.Search in Google Scholar
Croston, J. D. Forecasting and Stock Control for Intermittent Demands. – J. Oper. Res. Soc., Vol. 23, 1972, No 3, pp. 289-303. DOI: 10.1057/JORS.1972.50.Search in Google Scholar
Teunter, R. H., A. A. Syntetos, M. Z. Babai. Intermittent Demand: Linking Forecasting to Inventory Obsolescence. – Eur. J. Oper. Res., Vol. 214, 2011, No 3, pp. 606-615. DOI: 10.1016/j.ejor.2011.05.018.Search in Google Scholar
Babai, M. Z., A. Syntetos, R. Teunter. Intermittent Demand Forecasting: An Empirical Study on Accuracy and the Risk of Obsolescence. – Int. J. Prod. Econ., Vol. 157, 2014, No 1, pp. 212-219. DOI: 10.1016/J.IJPE.2014.08.019.Search in Google Scholar
Kalaya, P., P. Termsuksawad, T. Wasusri. Forecasting Lumpy Demand for Planning Inventory: The Case of Community Hospitals in Thailand. – In: Proc. of IEEE Int. Conf. Ind. Eng. Eng. Manag., December 2019, pp. 686-690. DOI: 10.1109/IEEM44572.2019.8978541.Search in Google Scholar
Bao, Y., R. Zhang, T. Xiong, Z. Hu. Forecasting Non-Normal Demand by Support Vector Machines with Ensemble Empirical Mode Decomposition. – Adv. Inf. Sci. Serv. Sci., Vol. 3, 2011, No 3, pp. 81-91. DOI: 10.4156/aiss.vol3.issue3.11.Search in Google Scholar
Wu, Z., N. E. Huang. Ensemble Empirical Mode Decomposition: A Noise-Assisted Data Analysis Method. – Adv. Adapt. Data Anal., Vol. 1, 2009, No 1, pp. 1-41. DOI: 10.1142/S1793536909000047.Search in Google Scholar
Sun, S. C., W. Guo. Forecasting Wireless Demand with Extreme Values Using Feature Embedding in Gaussian Processes. – In: Proc. of IEEE Veh. Technol. Conf., Vol. 2021, 2021, pp. 1-8. DOI: 10.1109/VTC2021-Spring51267.2021.9449040.Search in Google Scholar
Rana, M. R. R, A. Nawaz, T. Ali, A. S. Alattas, A. S. Abd Elminaam. Sentiment Analysis of Product Reviews Using Transformer Enhanced 1D-CNN and BiLSTM. – Cybernetics and Information Technologies, Vol. 24, 2024, No 3, pp. 112-131.Search in Google Scholar
Silverman, B. W. Monographs on Statistics and Applied Probability: Density Estimation for Statistics and Data Analysis. – Appl. Stat., Vol. 37, 1986, No 1, p. 120.Search in Google Scholar
Vietnam National Authority of Tourism (Accessed 18 November 2024). https://vietnamtourism.gov.vn/enSearch in Google Scholar
Vietnam: Coronavirus Pandemic Country Profile – Our World in Data (Accessed May 18, 2024). https://ourworldindata.org/coronavirus/country/vietnam?country=~VNMSearch in Google Scholar
Overseas Travel and Tourism Time Series – Office for National Statistics (Accessed 21 August, 2024). https://www.ons.gov.uk/peoplepopulationandcommunity/leisureandtourism/datasets/internationalpassengersurveytimeseriesspreadsheetSearch in Google Scholar
