Summary of Convlstmtransnet: a Hybrid Deep Learning Approach For Internet Traffic Telemetry, by Sajal Saha et al.
ConvLSTMTransNet: A Hybrid Deep Learning Approach for Internet Traffic Telemetry
by Sajal Saha, Saikat Das, Glaucio H.S. Carvalho
First submitted to arxiv on: 20 Sep 2024
Categories
- Main: Machine Learning (cs.LG)
- Secondary: None
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| Summary difficulty | Written by | Summary |
|---|---|---|
| High | Paper authors | High Difficulty Summary Read the original abstract here |
| Medium | GrooveSquid.com (original content) | Medium Difficulty Summary A novel hybrid deep learning model, ConvLSTMTransNet, is proposed for time series prediction in internet traffic telemetry. This model combines the strengths of CNNs, LSTMs, and Transformer encoders to capture complex spatial-temporal relationships in time series data. The model outperforms three baseline models (RNN, LSTM, and GRU) by approximately 10% in terms of prediction accuracy, using metrics such as MAE, RMSE, and WAPE. ConvLSTMTransNet’s innovative architecture enables it to capture temporal dependencies and extract spatial features from internet traffic data more effectively than traditional models. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary This paper presents a new way to predict internet traffic patterns. It uses a special kind of artificial intelligence called deep learning to make predictions about what will happen in the future. The model is better at making these predictions than other models because it can understand both how things change over time and what’s happening in different parts of the network. This helps us make more accurate predictions, which is important for managing internet traffic. |
Keywords
» Artificial intelligence » Deep learning » Lstm » Mae » Rnn » Time series » Transformer
