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

## TL;DR

TSXplain is a novel framework that generates natural language and statistical feature-based explanations for DNN decisions on time-series data, enhancing interpretability for both experts and novices.

## Contribution

It introduces a new approach combining statistical features with natural language to explain DNN decisions on time-series, bridging the interpretability gap for different user levels.

## Key findings

- Explanations are meaningful and correct based on survey and reliability tests.
- The system effectively aids both expert and novice users in understanding DNN decisions.
- Demonstrates the feasibility of natural language explanations for time-series neural network decisions.

## Abstract

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.