  Adapting to concept drift is a challenging task in machine learning, which is
usually tackled using incremental learning techniques that periodically re-fit
a learning model leveraging newly available data. A primary limitation of these
techniques is their reliance on substantial amounts of data for retraining. The
necessity of acquiring fresh data introduces temporal delays prior to
retraining, potentially rendering the models inaccurate if a sudden concept
drift occurs in-between two consecutive retrainings. In communication networks,
such issue emerges when performing traffic forecasting following a~failure
event: post-failure re-routing may induce a drastic shift in distribution and
pattern of traffic data, thus requiring a timely model adaptation. In this
work, we address this challenge for the problem of traffic forecasting and
propose an approach that exploits adaptive learning algorithms, namely, liquid
neural networks, which are capable of self-adaptation to abrupt changes in data
patterns without requiring any retraining. Through extensive simulations of
failure scenarios, we compare the predictive performance of our proposed
approach to that of a reference method based on incremental learning.
Experimental results show that our proposed approach outperforms incremental
learning-based methods in situations where the shifts in traffic patterns are
drastic.
