Don't Get Me Wrong: How to Apply Deep Visual Interpretations to Time Series

• URL: http://arxiv.org/abs/2203.07861v3
• Date: Thu, 10 Jul 2025 08:29:38 GMT
• Title: Don't Get Me Wrong: How to Apply Deep Visual Interpretations to Time Series
• Authors: Christoffer Loeffler, Wei-Cheng Lai, Bjoern Eskofier, Dario Zanca, Lukas Schmidt, Christopher Mutschler,
• Abstract summary: This paper investigates saliency methods on time series data to formulate recommendations for interpreting convolutional models.<n>We employ nine gradient-, propagation-, or perturbation-based post-hoc saliency methods across six varied and complex real-world datasets.
• Score: 4.457819501195421
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: The correct interpretation of convolutional models is a hard problem for time series data. While saliency methods promise visual validation of predictions for image and language processing, they fall short when applied to time series. These tend to be less intuitive and represent highly diverse data, such as the tool-use time series dataset. Furthermore, saliency methods often generate varied, conflicting explanations, complicating the reliability of these methods. Consequently, a rigorous objective assessment is necessary to establish trust in them. This paper investigates saliency methods on time series data to formulate recommendations for interpreting convolutional models and implements them on the tool-use time series problem. To achieve this, we first employ nine gradient-, propagation-, or perturbation-based post-hoc saliency methods across six varied and complex real-world datasets. Next, we evaluate these methods using five independent metrics to generate recommendations. Subsequently, we implement a case study focusing on tool-use time series using convolutional classification models. Our results validate our recommendations that indicate that none of the saliency methods consistently outperforms others on all metrics, while some are sometimes ahead. Our insights and step-by-step guidelines allow experts to choose suitable saliency methods for a given model and dataset.

Related papers

• Understanding the Limits of Deep Tabular Methods with Temporal Shift [28.738848567072004]
  We introduce a plug-and-play temporal embedding method based on Fourier series expansion to learn and incorporate temporal patterns.<n>Our experiments demonstrate that this temporal embedding, combined with the improved training protocol, provides a more effective and robust framework for learning from temporal data.
  arXiv  Detail & Related papers  (2025-02-27T16:48:53Z)
• Time Series Data Augmentation as an Imbalanced Learning Problem [2.5536554335016417]
  We use oversampling strategies to create synthetic time series observations and improve the accuracy of forecasting models. We carried out experiments using 7 different databases that contain a total of 5502 univariate time series. We found that the proposed solution outperforms both a global and a local model, thus providing a better trade-off between these two approaches.
  arXiv  Detail & Related papers  (2024-04-29T09:27:15Z)
• Chronos: Learning the Language of Time Series [79.38691251254173]
  Chronos is a framework for pretrained probabilistic time series models. We show that Chronos models can leverage time series data from diverse domains to improve zero-shot accuracy on unseen forecasting tasks.
  arXiv  Detail & Related papers  (2024-03-12T16:53:54Z)
• Parametric Augmentation for Time Series Contrastive Learning [33.47157775532995]
  We create positive examples that assist the model in learning robust and discriminative representations. Usually, preset human intuition directs the selection of relevant data augmentations. We propose a contrastive learning framework with parametric augmentation, AutoTCL, which can be adaptively employed to support time series representation learning.
  arXiv  Detail & Related papers  (2024-02-16T03:51:14Z)
• Revisiting Self-supervised Learning of Speech Representation from a Mutual Information Perspective [68.20531518525273]
  We take a closer look into existing self-supervised methods of speech from an information-theoretic perspective. We use linear probes to estimate the mutual information between the target information and learned representations. We explore the potential of evaluating representations in a self-supervised fashion, where we estimate the mutual information between different parts of the data without using any labels.
  arXiv  Detail & Related papers  (2024-01-16T21:13:22Z)
• What Makes Pre-Trained Visual Representations Successful for Robust Manipulation? [57.92924256181857]
  We find that visual representations designed for manipulation and control tasks do not necessarily generalize under subtle changes in lighting and scene texture. We find that emergent segmentation ability is a strong predictor of out-of-distribution generalization among ViT models.
  arXiv  Detail & Related papers  (2023-11-03T18:09:08Z)
• Contrastive Difference Predictive Coding [79.74052624853303]
  We introduce a temporal difference version of contrastive predictive coding that stitches together pieces of different time series data to decrease the amount of data required to learn predictions of future events. We apply this representation learning method to derive an off-policy algorithm for goal-conditioned RL.
  arXiv  Detail & Related papers  (2023-10-31T03:16:32Z)
• Generative Modeling of Regular and Irregular Time Series Data via Koopman VAEs [50.25683648762602]
  We introduce Koopman VAE, a new generative framework that is based on a novel design for the model prior. Inspired by Koopman theory, we represent the latent conditional prior dynamics using a linear map. KoVAE outperforms state-of-the-art GAN and VAE methods across several challenging synthetic and real-world time series generation benchmarks.
  arXiv  Detail & Related papers  (2023-10-04T07:14:43Z)
• Unsupervised Representation Learning for Time Series: A Review [20.00853543048447]
  Unsupervised representation learning approaches aim to learn discriminative feature representations from unlabeled data, without the requirement of annotating every sample. We conduct a literature review of existing rapidly evolving unsupervised representation learning approaches for time series. We empirically evaluate state-of-the-art approaches, especially the rapidly evolving contrastive learning methods, on 9 diverse real-world datasets.
  arXiv  Detail & Related papers  (2023-08-03T07:28:06Z)
• Multispectral Contrastive Learning with Viewmaker Networks [8.635434871127512]
  We focus on applying contrastive learning approaches to a variety of remote sensing datasets. We show that Viewmaker networks are promising for producing views in this setting without requiring extensive domain knowledge and trial and error.
  arXiv  Detail & Related papers  (2023-02-11T18:44:12Z)
• Differentiable Outlier Detection Enable Robust Deep Multimodal Analysis [20.316056261749946]
  We propose an end-to-end vision and language model incorporating explicit knowledge graphs. We also introduce an interactive out-of-distribution layer using implicit network operator. In practice, we apply our model on several vision and language downstream tasks including visual question answering, visual reasoning, and image-text retrieval.
  arXiv  Detail & Related papers  (2023-02-11T05:46:21Z)
• Localization vs. Semantics: Visual Representations in Unimodal and Multimodal Models [57.08925810659545]
  We conduct a comparative analysis of the visual representations in existing vision-and-language models and vision-only models. Our empirical observations suggest that vision-and-language models are better at label prediction tasks. We hope our study sheds light on the role of language in visual learning, and serves as an empirical guide for various pretrained models.
  arXiv  Detail & Related papers  (2022-12-01T05:00:18Z)
• ADATIME: A Benchmarking Suite for Domain Adaptation on Time Series Data [20.34427953468868]
  Unsupervised domain adaptation methods aim to generalize well on unlabeled test data that may have a different distribution from the training data. Existing works on time series domain adaptation suffer from inconsistencies in evaluation schemes, datasets, and backbone neural network architectures. We develop a benchmarking evaluation suite (AdaTime) to systematically and fairly evaluate different domain adaptation methods on time series data.
  arXiv  Detail & Related papers  (2022-03-15T23:55:05Z)
• TACTiS: Transformer-Attentional Copulas for Time Series [76.71406465526454]
  estimation of time-varying quantities is a fundamental component of decision making in fields such as healthcare and finance. We propose a versatile method that estimates joint distributions using an attention-based decoder. We show that our model produces state-of-the-art predictions on several real-world datasets.
  arXiv  Detail & Related papers  (2022-02-07T21:37:29Z)
• Fair Interpretable Learning via Correction Vectors [68.29997072804537]
  We propose a new framework for fair representation learning centered around the learning of "correction vectors" The corrections are then simply summed up to the original features, and can therefore be analyzed as an explicit penalty or bonus to each feature. We show experimentally that a fair representation learning problem constrained in such a way does not impact performance.
  arXiv  Detail & Related papers  (2022-01-17T10:59:33Z)
• Mimic: An adaptive algorithm for multivariate time series classification [11.49627617337276]
  Time series data are valuable but are often inscrutable. Gaining trust in time series classifiers for finance, healthcare, and other critical applications may rely on creating interpretable models. We propose a novel Mimic algorithm that retains the predictive accuracy of the strongest classifiers while introducing interpretability.
  arXiv  Detail & Related papers  (2021-11-08T04:47:31Z)
• Self-Supervised Visual Representation Learning Using Lightweight Architectures [0.0]
  In self-supervised learning, a model is trained to solve a pretext task, using a data set whose annotations are created by a machine. We critically examine the most notable pretext tasks to extract features from image data. We study the performance of various self-supervised techniques keeping all other parameters uniform.
  arXiv  Detail & Related papers  (2021-10-21T14:13:10Z)
• Revisiting Contrastive Methods for Unsupervised Learning of Visual Representations [78.12377360145078]
  Contrastive self-supervised learning has outperformed supervised pretraining on many downstream tasks like segmentation and object detection. In this paper, we first study how biases in the dataset affect existing methods. We show that current contrastive approaches work surprisingly well across: (i) object- versus scene-centric, (ii) uniform versus long-tailed and (iii) general versus domain-specific datasets.
  arXiv  Detail & Related papers  (2021-06-10T17:59:13Z)
• Visualising Deep Network's Time-Series Representations [93.73198973454944]
  Despite the popularisation of machine learning models, more often than not they still operate as black boxes with no insight into what is happening inside the model. In this paper, a method that addresses that issue is proposed, with a focus on visualising multi-dimensional time-series data. Experiments on a high-frequency stock market dataset show that the method provides fast and discernible visualisations.
  arXiv  Detail & Related papers  (2021-03-12T09:53:34Z)
• Time Series Data Imputation: A Survey on Deep Learning Approaches [4.4458738910060775]
  Time series data imputation is a well-studied problem with different categories of methods. Time series methods based on deep learning have made progress with the usage of models like RNN. We will review and discuss their model architectures, their pros and cons as well as their effects to show the development of the time series imputation methods.
  arXiv  Detail & Related papers  (2020-11-23T11:57:27Z)
• Evaluating the Disentanglement of Deep Generative Models through Manifold Topology [66.06153115971732]
  We present a method for quantifying disentanglement that only uses the generative model. We empirically evaluate several state-of-the-art models across multiple datasets.
  arXiv  Detail & Related papers  (2020-06-05T20:54:11Z)

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.