  Prolonging the lifetime of massive machine-type communication (MTC) networks
is key to realizing a sustainable digitized society. Great energy savings can
be achieved by accurately predicting MTC traffic followed by properly designed
resource allocation mechanisms. However, selecting the proper MTC traffic
predictor is not straightforward and depends on accuracy/complexity trade-offs
and the specific MTC applications and network characteristics. Remarkably, the
related state-of-the-art literature still lacks such debates. Herein, we assess
the performance of several machine learning (ML) methods to predict Poisson and
quasi-periodic MTC traffic in terms of accuracy and computational cost. Results
show that the temporal convolutional network (TCN) outperforms the long-short
term memory (LSTM), the gated recurrent units (GRU), and the recurrent neural
network (RNN), in that order. For Poisson traffic, the accuracy gap between the
predictors is larger than under quasi-periodic traffic. Finally, we show that
running a TCN predictor is around three times more costly than other methods,
while the training/inference time is the greatest/least.
