AIChronoLens: AI/ML Explainability for Time Series Forecasting in Mobile Networks

Forecasting is increasingly considered a fundamental enabler for the management of next-generation mobile networks. While deep neural networks excel at short- and long-term forecasting, their complexity hinders interpretability, a crucial factor for production deployment. The existing EXplainable Artificial Intelligence (XAI) techniques, primarily designed for computer vision and natural language processing, struggle with time series data due to their lack of understanding of temporal characteristics of the input data. In this paper, we take the research on EXplainable Artificial Intelligence (XAI) for time series forecasting one step further by proposing <sc xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">AIChronoLens</small>, a new tool that links legacy XAI explanations with the temporal properties of the input. <sc xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">AIChronoLens</small> allows diving deep into the behavior of time series predictors and spotting, among other aspects, the hidden causes of forecast errors. We show that <sc xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">AIChronoLens</small>’s output can be utilized for meta-learning to predict when the original time series forecasting model makes errors and fix them in advance, thereby improving the accuracy of the predictors. Extensive evaluations with real-world mobile traffic traces pinpoint model behaviors that would not be possible to identify otherwise and show how model performance can be improved by 32 % upon re-training and by up to 39 % with meta-learning.