Explainable artificial intelligence for time series modelling and causal inference

Due to the concern over the trust issue of black-box artificial intelligence (AI) in daily human life, “right to an explanation” requirement is hence proposed for any algorithm in Europe general data protection regulation (GDPR). However, the framework of explain- able AI (XAI) is still in its infancy. Thus, this thesis aims to propose a framework of explainable AI for time series (X-AI4TS) in neural modelling and causal inference. In order to achieve the trustworthy usage of algorithm, at the first stage, this thesis embed the priori knowledge in time series and break it down into neuron-based dynamic system with domain expertise for explainable modelling. Then secondly, this thesis ad- dress the explainabilty in causal inference steps of the neuron-based model — (i) Identifying the “hard to learn” features for neurons; (ii) Revealing and visualizing the inner operations within neurons; (iii) Inferring the original posteriori multimodal distribution via neurons. In addition, the aforementioned methodologies are formulated into an X-AI4TS frame- work and applied to a real business case in semiconductor fabrication for an integrated implementation. A data-driven neuron-based time-series forecasting digital twin is generated, under my proposed framework, for a dynamic manufacturing system. The results offer the diverse explainability to different users (i.e., end-users, data scientist and AI expert) and show the landing potential in dynamic engineering time series application.