A Bag of Receptive Fields for Time Series Extrinsic Predictions

High-dimensional time series data poses challenges due to its dynamic nature, varying lengths, and presence of missing values. This kind of data requires extensive preprocessing, limiting the applicability of existing Time Series Classification and Time Series Extrinsic Regression techniques. For this reason, we propose BORF, a Bag-Of-Receptive-Fields model, which incorporates notions from time series convolution and 1D-SAX to handle univariate and multivariate time series with varying lengths and missing values. We evaluate BORF on Time Series Classification and Time Series Extrinsic Regression tasks using the full UEA and UCR repositories, demonstrating its competitive performance against state-of-the-art methods. Finally, we outline how this representation can naturally provide saliency and feature-based explanations.

A Protocol for Continual Explanation of SHAP

Continual Learning trains models on a stream of data, with the aim of learning new information without forgetting previous knowledge. Given the dynamic nature of such environments, explaining the predictions of these models can be challenging. We study the behavior of SHAP values explanations in Continual Learning and propose an evaluation protocol to robustly assess the change of explanations in Class-Incremental scenarios. We observed that, while Replay strategies enforce the stability of SHAP values in feedforward/convolutional models, they are not able to do the same with fully-trained recurrent models. We show that alternative recurrent approaches, like randomized recurrent models, are more effective in keeping the explanations stable over time.

Modeling Events and Interactions through Temporal Processes -- A Survey

In real-world scenario, many phenomena produce a collection of events that occur in continuous time. Point Processes provide a natural mathematical framework for modeling these sequences of events. In this survey, we investigate probabilistic models for modeling event sequences through temporal processes. We revise the notion of event modeling and provide the mathematical foundations that characterize the literature on the topic. We define an ontology to categorize the existing approaches in terms of three families: simple, marked, and spatio-temporal point processes. For each family, we systematically review the existing approaches based based on deep learning. Finally, we analyze the scenarios where the proposed techniques can be used for addressing prediction and modeling aspects.