Capacity forecasting: traffic prediction with time-series ML

Time-Series Forecasting for Network Traffic

Network capacity forecasting predicts traffic demand at cell, cluster, and regional levels over horizons ranging from hours to years. Classical time-series methods like ARIMA and exponential smoothing capture linear trends and seasonality but struggle with complex non-linear patterns. Modern ML approaches -- LSTM networks, temporal convolutional networks (TCN), and transformer architectures -- learn intricate temporal dependencies including multi-seasonal patterns (daily, weekly, monthly), trend shifts, and the impact of external events. Feature engineering incorporates exogenous variables:…