MCNS: Mining Causal Natural Structures Inside Time Series via A Novel Internal Causality Scheme

Causal inference permits us to discover covert relationships of various variables in time series. However, in most existing works, the variables mentioned above are the dimensions. The causality between dimensions could be cursory, which hinders the comprehension of the internal relationship and the benefit of the causal graph to the neural networks (NNs). In this paper, we find that causality exists not only outside but also inside the time series because it reflects a succession of events in the real world. It inspires us to seek the relationship between internal subsequences. However, the challenges are the hardship of discovering causality from subsequences and utilizing the causal natural structures to improve NNs. To address these challenges, we propose a novel framework called Mining Causal Natural Structure (MCNS), which is automatic and domain-agnostic and helps to find the causal natural structures inside time series via the internal causality scheme. We evaluate the MCNS framework and impregnation NN with MCNS on time series classification tasks. Experimental results illustrate that our impregnation, by refining attention, shape selection classification, and pruning datasets, drives NN, even the data itself preferable accuracy and interpretability. Besides, MCNS provides an in-depth, solid summary of the time series and datasets.

Meta Pattern Concern Score: A Novel Metric for Customizable Evaluation of Multi-classification

Classifiers have been widely implemented in practice, while how to evaluate them properly remains a problem. Commonly used two types of metrics respectively based on confusion matrix and loss function have different advantages in flexibility and mathematical completeness, while they struggle in different dilemmas like the insensitivity to slight improvements or the lack of customizability in different tasks. In this paper, we propose a novel metric named Meta Pattern Concern Score based on the abstract representation of the probabilistic prediction, as well as the targeted design for processing negative classes in multi-classification and reducing the discreteness of metric value, to achieve advantages of both the two kinds of metrics and avoid their weaknesses. Our metric provides customizability to pick out the model for specific requirements in different practices, and make sure it is also fine under traditional metrics at the same time. Evaluation in four kinds of models and six datasets demonstrates the effectiveness and efficiency of our metric, and a case study shows it can select a model to reduce 0.53% of dangerous misclassifications by sacrificing only 0.04% of training accuracy.

TSFool: Crafting High-quality Adversarial Time Series through Multi-objective Optimization to Fool Recurrent Neural Network Classifiers

Deep neural network (DNN) classifiers are vulnerable to adversarial attacks. Although the existing gradient-based attacks have achieved good performance in feed-forward model and image recognition tasks, the extension for time series classification in the recurrent neural network (RNN) remains a dilemma, because the cyclical structure of RNN prevents direct model differentiation and the visual sensitivity to perturbations of time series data challenges the traditional local optimization objective to minimize perturbation. In this paper, an efficient and widely applicable approach called TSFool for crafting high-quality adversarial time series for the RNN classifier is proposed. We propose a novel global optimization objective named Camouflage Coefficient to consider how well the adversarial samples hide in class clusters, and accordingly redefine the high-quality adversarial attack as a multi-objective optimization problem. We also propose a new idea to use intervalized weighted finite automata (IWFA) to capture deeply embedded vulnerable samples having otherness between features and latent manifold to guide the approximation to the optimization solution. Experiments on 22 UCR datasets are conducted to confirm that TSFool is a widely effective, efficient and high-quality approach with 93.22% less local perturbation, 32.33% better global camouflage, and 1.12 times speedup to existing methods.