This paper examines the application of time series models for network load forecasting based on artificial intelligence. Traditional statistical methods and deep learning models are comparatively analyzed, and the effectiveness of the LSTM - based approach is scientifically evaluated [2].

artificial intelligence, time series, network load, LSTM, forecasting, machine learning.

Box, G. E. P., Jenkins, G. M., Reinsel, G. C., Ljung, G. M. (2015). Time Series Analysis: Forecasting and Control. 5th ed., Wiley.

Hochreiter, S., Schmidhuber, J. (1997). Long Short-Term Memory. Neural Computation, 9(8), 1735–1780.

Goodfellow, I., Bengio, Y., Courville, A. (2016). Deep Learning. MIT Press.

Cisco Systems (2023). Cisco Annual Internet Report (2018–2023) White Paper. Cisco.

Zhang, G. P. (2003). Time series forecasting using a hybrid ARIMA and neural network model. Neurocomputing, 50, 159 - 175.

Graves, A. (2012). Supervised Sequence Learning with Recurrent Neural Networks. Springer.

Brownlee, J. (2017). Deep Learning for Time Series Forecasting. Machine Learning Mastery.

Vaswani, A., Shazeer, N., Parmar, N., et al. (2017). Attention Is All You Need. Advances in Neural Information Processing Systems (NeurIPS), 30.

Hamilton, J. D. (1994). Time Series Analysis. Princeton University Press.

Kim, T.-Y., Cho, S.-B. (2019). Predicting residential energy consumption using 10.CNN - LSTM neural networks. Energy, 182, 72-81.