AFS-FCM With Memory: A Model for Air Quality Multi-Dimensional Prediction With Interpretability

Abstract

In order to represent the influences of different semantics on targets and improve the prediction with interpretability ability for multi-dimensional time series, we integrate Axiomatic Fuzzy Set (AFS) and Fuzzy Cognitive Map (FCM) with memory for fuzzy knowledge representation and prediction in this paper. The AFS is used to extract semantics of concepts for fuzzy representation using data distribution. The FCM with memory is trained to model the influence relationships between different semantics of concepts and multiple targets based on multi-dimensional time series data. And a multi- dimensional learning algorithm of AFS-FCM with memory based on gradient descent is developed to investigate the influences of different semantics of concepts on multiple targets. Finally, we validate our model by comparing with other FCMs, intrinsic interpretable models and machine learning methods for prediction of air quality multidimensional time series data, and discuss the performance of AFS-FCM with different transformation functions. The model can not only predict air quality accurately, but also explicitly reveal the specific quantitative relationship of different semantics of meteorology on air quality.