M-CELS: Counterfactual Explanation for Multivariate Time Series Data Guided by Learned Saliency Maps

• URL: http://arxiv.org/abs/2411.02649v1
• Date: Mon, 04 Nov 2024 22:16:24 GMT
• Title: M-CELS: Counterfactual Explanation for Multivariate Time Series Data Guided by Learned Saliency Maps
• Authors: Peiyu Li, Omar Bahri, Soukaina Filali Boubrahimi, Shah Muhammad Hamdi,
• Abstract summary: We introduce M-CELS, a counterfactual explanation model designed to enhance interpretability in multidimensional time series classification tasks. Results demonstrate the superior performance of M-CELS in terms of validity, proximity, and sparsity.
• Score: 0.9374652839580181
• License:
• Abstract: Over the past decade, multivariate time series classification has received great attention. Machine learning (ML) models for multivariate time series classification have made significant strides and achieved impressive success in a wide range of applications and tasks. The challenge of many state-of-the-art ML models is a lack of transparency and interpretability. In this work, we introduce M-CELS, a counterfactual explanation model designed to enhance interpretability in multidimensional time series classification tasks. Our experimental validation involves comparing M-CELS with leading state-of-the-art baselines, utilizing seven real-world time-series datasets from the UEA repository. The results demonstrate the superior performance of M-CELS in terms of validity, proximity, and sparsity, reinforcing its effectiveness in providing transparent insights into the decisions of machine learning models applied to multivariate time series data.

Related papers

• Harnessing Vision Models for Time Series Analysis: A Survey [72.09716244582684]
  This survey discusses the advantages of vision models over LLMs in time series analysis. It provides a comprehensive and in-depth overview of the existing methods, with dual views of detailed taxonomy. We address the challenges in the pre- and post-processing steps involved in this framework.
  arXiv  Detail & Related papers  (2025-02-13T00:42:11Z)
• Large Language Models are Few-shot Multivariate Time Series Classifiers [23.045734479292356]
  Large Language Models (LLMs) have been extensively applied in time series analysis. Yet, their utility in the few-shot classification (i.e., a crucial training scenario) is underexplored. We aim to leverage the extensive pre-trained knowledge in LLMs to overcome the data scarcity problem.
  arXiv  Detail & Related papers  (2025-01-30T03:59:59Z)
• VSFormer: Value and Shape-Aware Transformer with Prior-Enhanced Self-Attention for Multivariate Time Series Classification [47.92529531621406]
  We propose a novel method, VSFormer, that incorporates both discriminative patterns (shape) and numerical information (value) In addition, we extract class-specific prior information derived from supervised information to enrich the positional encoding. Extensive experiments on all 30 UEA archived datasets demonstrate the superior performance of our method compared to SOTA models.
  arXiv  Detail & Related papers  (2024-12-21T07:31:22Z)
• Hierarchical Multimodal LLMs with Semantic Space Alignment for Enhanced Time Series Classification [4.5939667818289385]
  HiTime is a hierarchical multi-modal model that seamlessly integrates temporal information into large language models. Our findings highlight the potential of integrating temporal features into LLMs, paving the way for advanced time series analysis.
  arXiv  Detail & Related papers  (2024-10-24T12:32:19Z)
• RA-BLIP: Multimodal Adaptive Retrieval-Augmented Bootstrapping Language-Image Pre-training [55.54020926284334]
  Multimodal Large Language Models (MLLMs) have recently received substantial interest, which shows their emerging potential as general-purpose models for various vision-language tasks. Retrieval augmentation techniques have proven to be effective plugins for both LLMs and MLLMs. In this study, we propose multimodal adaptive Retrieval-Augmented Bootstrapping Language-Image Pre-training (RA-BLIP), a novel retrieval-augmented framework for various MLLMs.
  arXiv  Detail & Related papers  (2024-10-18T03:45:19Z)
• Understanding Different Design Choices in Training Large Time Series Models [71.20102277299445]
  Training Large Time Series Models (LTSMs) on heterogeneous time series data poses unique challenges. We propose emphtime series prompt, a novel statistical prompting strategy tailored to time series data. We introduce textttLTSM-bundle, which bundles the best design choices we have identified.
  arXiv  Detail & Related papers  (2024-06-20T07:09:19Z)
• Time-LLM: Time Series Forecasting by Reprogramming Large Language Models [110.20279343734548]
  Time series forecasting holds significant importance in many real-world dynamic systems. We present Time-LLM, a reprogramming framework to repurpose large language models for time series forecasting. Time-LLM is a powerful time series learner that outperforms state-of-the-art, specialized forecasting models.
  arXiv  Detail & Related papers  (2023-10-03T01:31:25Z)
• Ti-MAE: Self-Supervised Masked Time Series Autoencoders [16.98069693152999]
  We propose a novel framework named Ti-MAE, in which the input time series are assumed to follow an integrate distribution. Ti-MAE randomly masks out embedded time series data and learns an autoencoder to reconstruct them at the point-level. Experiments on several public real-world datasets demonstrate that our framework of masked autoencoding could learn strong representations directly from the raw data.
  arXiv  Detail & Related papers  (2023-01-21T03:20:23Z)
• Multi-scale Attention Flow for Probabilistic Time Series Forecasting [68.20798558048678]
  We propose a novel non-autoregressive deep learning model, called Multi-scale Attention Normalizing Flow(MANF) Our model avoids the influence of cumulative error and does not increase the time complexity. Our model achieves state-of-the-art performance on many popular multivariate datasets.
  arXiv  Detail & Related papers  (2022-05-16T07:53:42Z)
• Multi-Time Attention Networks for Irregularly Sampled Time Series [18.224344440110862]
  Irregular sampling occurs in many time series modeling applications. We propose a new deep learning framework for this setting that we call Multi-Time Attention Networks. Our results show that our approach performs as well or better than a range of baseline and recently proposed models.
  arXiv  Detail & Related papers  (2021-01-25T18:57:42Z)

This list is automatically generated from the titles and abstracts of the papers in this site.

This site does not guarantee the quality of this site (including all information) and is not responsible for any consequences.