Advanced Network Traffic Prediction Using Deep Learning Techniques: A Comparative Study of SVR, LSTM, GRU, and Bidirectional LSTM Models
Accurate prediction of network traffic patterns is essential for optimizing network resource allocation, managing congestion, and strengthening cybersecurity. This study examines the effectiveness of four machine learning models—Support Vector Regression (SVR), Long Short-Term Memory (LSTM), Gated R...
Saved in:
Main Author: | Wang Yuxin |
---|---|
Format: | Article |
Language: | English |
Published: |
EDP Sciences
2025-01-01
|
Series: | ITM Web of Conferences |
Online Access: | https://www.itm-conferences.org/articles/itmconf/pdf/2025/01/itmconf_dai2024_03021.pdf |
Tags: |
Add Tag
No Tags, Be the first to tag this record!
|
Similar Items
-
Classification of User Expressions on Social Media Using LSTM and GRU Models
by: I Gede Putra Mas Yusadara, et al.
Published: (2025-01-01) -
Comparing ChatGPT And LSTM In Predicting Changes In Quarterly Financial Metrics
by: Vitali Chaiko
Published: (2024-06-01) -
Enhancing Stock Price Forecasting Accuracy Using LSTM and Bi-LSTM Models
by: Wang Hao
Published: (2025-01-01) -
Leveraging Quantum LSTM for High-Accuracy Prediction of Viral Mutations
by: Prashanth Choppara, et al.
Published: (2025-01-01) -
Robot Dynamic Path Planning Based on Prioritized Experience Replay and LSTM Network
by: Hongqi Li, et al.
Published: (2025-01-01)