Published March 1, 2026
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ARTIFICIAL INTELLIGENCE - BASED TIME SERIES MODEL FOR NETWORK LOAD FORECASTING
Authors/Creators
- 1. Lecturer at the University of Economics and Pedagogy
Description
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].
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2012-2019.pdf
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Additional details
References
- 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.