Overcoming data limitations in internet traffic forecasting: LSTM models with transfer learning and wavelet augmentation

Accurate internet traffic prediction in smaller ISP networks is challenged by limited data availability. This paper explores this issue using transfer learning and data augmentation techniques with two LSTM-based models, LSTMSeq2Seq and LSTMSeq2SeqAtn, initially trained on a comprehensive dataset provided by Juniper Networks, Inc. and subsequently applied to smaller datasets. The datasets represent real internet traffic telemetry, offering insights into diverse traffic patterns across different network domains. Our study found that although both models performed well in single-step predictions, multi-step forecasting was more challenging, especially regarding long-term accuracy. Empirical results demonstrated that LSTMSeq2Seq outperformed LSTMSeq2SeqAtn on smaller datasets, with improvements in forecasting accuracy by up to 36.70% in MAE and 27.66% in WAPE after applying data augmentation using Discrete Wavelet Transform. The LSTMSeq2Seq model achieved an accuracy improvement from 83% to 88% for 6-step forecasts, 82% to 88% for 9-step forecasts, and 81% to 87% for 12-step forecasts, whereas LSTMSeq2SeqAtn exhibited a more stable short-term performance but higher variability in longer forecasts. Additionally, the mean absolute percentage error (MAPE) of multi-step predictions increased over longer horizons, with LSTMSeq2Seq reaching 6.74% at 12 steps and LSTMSeq2SeqAtn at 6.77%, highlighting the challenge of long-term forecasting. Variability analysis showed that while the attention mechanism in LSTMSeq2SeqAtn improved short-term prediction consistency, it also increased uncertainty in longer forecasts, as seen in the interquartile range (IQR) rising from 0.578 at 6 steps to 1.237 at 9 steps. Outlier analysis further confirmed that LSTMSeq2Seq exhibited more stable improvements, whereas LSTMSeq2SeqAtn showed increased dispersion in forecast accuracy. These findings underscore the importance of transfer learning and data augmentation in enhancing forecasting accuracy, particularly for smaller ISP networks with limited data availability. Furthermore, our analysis highlights the trade-offs between model complexity, short-term consistency, and long-term stability in internet traffic prediction.

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