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Explanation methods applied to sequential models for multivariate time series prediction are receiving more attention in machine learning literature. While current methods perform well at providing instance-wise explanations, they struggle to efficiently and accurately make attributions over long periods of time and with complex feature interactions. We propose WinIT, a framework for evaluating feature importance in time series prediction settings by quantifying the shift in predictive distribution over multiple instances in a windowed setting. Comprehensive empirical evidence shows our method improves on the previous state-of-the-art, FIT, by capturing temporal dependencies in feature importance. We also demonstrate how the solution improves the appropriate attribution of features within time steps, which existing interpretability methods often fail to do. We compare with baselines on simulated and real-world clinical data. WinIT achieves 2.47x better performance than FIT and other feature importance methods on real-world clinical MIMIC-mortality task. The code for this work is available at https://github.com/layer6ai-labs/WinIT.
Recurrent neural networks (RNNs) with continuous-time hidden states are a natural fit for modeling irregularly-sampled time series. These models, however, face difficulties when the input data possess long-term dependencies. We prove that similar to
This work presents an introduction to feature-based time-series analysis. The time series as a data type is first described, along with an overview of the interdisciplinary time-series analysis literature. I then summarize the range of feature-based
Learning representations that accurately capture long-range dependencies in sequential inputs -- including text, audio, and genomic data -- is a key problem in deep learning. Feed-forward convolutional models capture only feature interactions within
How can we explain the predictions of a machine learning model? When the data is structured as a multivariate time series, this question induces additional difficulties such as the necessity for the explanation to embody the time dependency and the l
We introduce supervised feature ranking and feature subset selection algorithms for multivariate time series (MTS) classification. Unlike most existing supervised/unsupervised feature selection algorithms for MTS our techniques do not require a featu