# TSXplain: Demystification of DNN Decisions for Time-Series using Natural Language and Statistical Features

arXiv: https://arxiv.org/abs/1905.06175 · HTML: https://mlsanity.com/paper/1905.06175/

## Key facts

- Cited by 5 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 15 May 2019.

## Built on by (most cited first, 5 of 5)

- [Explainable Artificial Intelligence (XAI) on TimeSeries Data: A Survey](https://mlsanity.com/paper/2104.00950/) (2021)
- [A Survey on XAI for 5G and Beyond Security: Technical Aspects, Challenges and Research Directions](https://mlsanity.com/paper/2204.12822/) (2022)
- [Improving the Evaluation and Actionability of Explanation Methods for Multivariate Time Series Classification](https://mlsanity.com/paper/2406.12507/) (2024)
- [PUPAE: Intuitive and Actionable Explanations for Time Series Anomalies](https://mlsanity.com/paper/2401.09489/) (2024)
- [SSET: Swapping-Sliding Explanation for Time Series Classifiers in Affect Detection](https://mlsanity.com/paper/2410.12996/) (2024)

## Abstract (arXiv)

Neural networks (NN) are considered as black-boxes due to the lack of explainability and transparency of their decisions. This significantly hampers their deployment in environments where explainability is essential along with the accuracy of the system. Recently, significant efforts have been made for the interpretability of these deep networks with the aim to open up the black-box. However, most of these approaches are specifically developed for visual modalities. In addition, the interpretations provided by these systems require expert knowledge and understanding for intelligibility. This indicates a vital gap between the explainability provided by the systems and the novice user. To bridge this gap, we present a novel framework i.e. Time-Series eXplanation (TSXplain) system which produces a natural language based explanation of the decision taken by a NN. It uses the extracted statistical features to describe the decision of a NN, merging the deep learning world with that of statistics. The two-level explanation provides ample description of the decision made by the network to aid an expert as well as a novice user alike. Our survey and reliability assessment test confirm that the generated explanations are meaningful and correct. We believe that generating natural language based descriptions of the network's decisions is a big step towards opening up the black-box.