Network traffic forecasting estimates future network traffic based on historical traffic observations. It has a wide range of applications, and substantial attention has been dedicated to this research area. Cellular networks serve as the backbone of modern-day communication systems, which support billions of users throughout the world and can help improve the quality of urban life to a great extent. Therefore, accurate traffic prediction is becoming more important for network planning, control management, and the Quality of Service. Diverse methods, including neural network-based methods and data mining methods, have been used for this goal. The Recurrent Neural family is well known for time series data modeling, which predicts the future time series based on the historical data being fed as input to neural nets which may have large time lags with variable lengths. RNN includes several network architectures, such as vanilla RNN and Long Short Term memory (LSTM), that can learn temporal patterns and long-term dependencies in vast sequences of arbitrary length. This paper proposes three models based on LSTM architecture, a multi-layer LSTM with Auto-Encoder, and an AE-LSTM combined with a Multi-Layer Perceptron neural network. The results of each model are discussed in the paper. Simulation outcomes were implemented in Python and compared to existing algorithms, demonstrating high efficacy and performance.
| Identifer | oai:union.ndltd.org:UPSALLA1/oai:DiVA.org:uu-511375 |
| Date | January 2023 |
| Creators | Singh, Gaurav, Singh, Shreyansh |
| Publisher | Uppsala universitet, Institutionen för informationsteknologi |
| Source Sets | DiVA Archive at Upsalla University |
| Language | English |
| Detected Language | English |
| Type | Student thesis, info:eu-repo/semantics/bachelorThesis, text |
| Format | application/pdf |
| Rights | info:eu-repo/semantics/openAccess |
| Relation | IT ; 23068 |
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