# TSViz: Demystification of Deep Learning Models for Time-Series Analysis

arXiv: https://arxiv.org/abs/1802.02952 · HTML: https://mlsanity.com/paper/1802.02952/

## Key facts

- Cited by 25 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 8 Feb 2018.

## Built on by (most cited first, 25 of 25)

- [Explaining Time Series Predictions with Dynamic Masks](https://mlsanity.com/paper/2106.05303/) (2021)
- [Explainable Artificial Intelligence (XAI) on TimeSeries Data: A Survey](https://mlsanity.com/paper/2104.00950/) (2021)
- [Explainable Artificial Intelligence: A Survey of Needs, Techniques, Applications, and Future Direction](https://mlsanity.com/paper/2409.00265/) (2024)
- [TS-MULE: Local Interpretable Model-Agnostic Explanations for Time Series Forecast Models](https://mlsanity.com/paper/2109.08438/) (2021)
- [Explainable AI for clinical and remote health applications: a survey on tabular and time series data](https://mlsanity.com/paper/2209.06528/) (2022)
- [Explainable AI for Time Series via Virtual Inspection Layers](https://mlsanity.com/paper/2303.06365/) (2023)
- [Interpretation of Time-Series Deep Models: A Survey](https://mlsanity.com/paper/2305.14582/) (2023)
- [Privacy Meets Explainability: A Comprehensive Impact Benchmark](https://mlsanity.com/paper/2211.04110/) (2022)
- [Time Series Model Attribution Visualizations as Explanations](https://mlsanity.com/paper/2109.12935/) (2021)
- [Time to Focus: A Comprehensive Benchmark Using Time Series Attribution Methods](https://mlsanity.com/paper/2202.03759/) (2022)
- [Adversarial Framework with Certified Robustness for Time-Series Domain via Statistical Features](https://mlsanity.com/paper/2207.04307/) (2022)
- [TimeREISE: Time-series Randomized Evolving Input Sample Explanation](https://mlsanity.com/paper/2202.07952/) (2022)
- [On the Soundness of XAI in Prognostics and Health Management (PHM)](https://mlsanity.com/paper/2303.05517/) (2023)
- [Towards an Awareness of Time Series Anomaly Detection Models' Adversarial Vulnerability](https://mlsanity.com/paper/2208.11264/) (2022)
- [Visual Explanations with Attributions and Counterfactuals on Time Series Classification](https://mlsanity.com/paper/2307.08494/) (2023)
- [TSInterpret: A unified framework for time series interpretability](https://mlsanity.com/paper/2208.05280/) (2022)
- [CAFO: Feature-Centric Explanation on Time Series Classification](https://mlsanity.com/paper/2406.01833/) (2024)
- [XTSC-Bench: Quantitative Benchmarking for Explainers on Time Series Classification](https://mlsanity.com/paper/2310.14957/) (2023)
- [PatchX: Explaining Deep Models by Intelligible Pattern Patches for Time-series Classification](https://mlsanity.com/paper/2102.05917/) (2021)
- [Interpreting Outliers in Time Series Data through Decoding Autoencoder](https://mlsanity.com/paper/2409.01713/) (2024)
- [SigTime: Learning and Visually Explaining Time Series Signatures](https://mlsanity.com/paper/2512.12076/) (2025)
- [Statistics and Deep Learning-based Hybrid Model for Interpretable Anomaly Detection](https://mlsanity.com/paper/2202.12720/) (2022)
- [Evaluating Simplification Algorithms for Interpretability of Time Series Classification](https://mlsanity.com/paper/2505.08846/) (2025)
- [FREQuency ATTribution: benchmarking frequency-based occlusion for time series data](https://mlsanity.com/paper/2506.18481/) (2025)
- [Server-side Anti-cheat in FPS games for Aimbot detection using Deep learning and Machine learning](https://mlsanity.com/paper/2607.04336/) (2026)

## Abstract (arXiv)

This paper presents a novel framework for demystification of convolutional deep learning models for time-series analysis. This is a step towards making informed/explainable decisions in the domain of time-series, powered by deep learning. There have been numerous efforts to increase the interpretability of image-centric deep neural network models, where the learned features are more intuitive to visualize. Visualization in time-series domain is much more complicated as there is no direct interpretation of the filters and inputs as compared to the image modality. In addition, little or no concentration has been devoted for the development of such tools in the domain of time-series in the past. TSViz provides possibilities to explore and analyze a network from different dimensions at different levels of abstraction which includes identification of parts of the input that were responsible for a prediction (including per filter saliency), importance of different filters present in the network for a particular prediction, notion of diversity present in the network through filter clustering, understanding of the main sources of variation learnt by the network through inverse optimization, and analysis of the network's robustness against adversarial noise. As a sanity check for the computed influence values, we demonstrate results regarding pruning of neural networks based on the computed influence information. These representations allow to understand the network features so that the acceptability of deep networks for time-series data can be enhanced. This is extremely important in domains like finance, industry 4.0, self-driving cars, health-care, counter-terrorism etc., where reasons for reaching a particular prediction are equally important as the prediction itself. We assess the proposed framework for interpretability with a set of desirable properties essential for any method.