FIC-TSC: Learning Time Series Classification with Fisher Information Constraint

• URL: http://arxiv.org/abs/2505.06114v1
• Date: Fri, 09 May 2025 15:13:27 GMT
• Title: FIC-TSC: Learning Time Series Classification with Fisher Information Constraint
• Authors: Xiwen Chen, Wenhui Zhu, Peijie Qiu, Hao Wang, Huayu Li, Zihan Li, Yalin Wang, Aristeidis Sotiras, Abolfazl Razi,
• Abstract summary: Analyzing time series data is crucial to a wide spectrum of applications, including economics, online marketplaces, and human healthcare.<n>Time series data often suffers from domain shifts between training and test sets, which dramatically degrades the classification performance.<n>We propose textitFIC-TSC, a training framework for time series classification that leverages Fisher information as the constraint.
• Score: 8.414596562236326
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: Analyzing time series data is crucial to a wide spectrum of applications, including economics, online marketplaces, and human healthcare. In particular, time series classification plays an indispensable role in segmenting different phases in stock markets, predicting customer behavior, and classifying worker actions and engagement levels. These aspects contribute significantly to the advancement of automated decision-making and system optimization in real-world applications. However, there is a large consensus that time series data often suffers from domain shifts between training and test sets, which dramatically degrades the classification performance. Despite the success of (reversible) instance normalization in handling the domain shifts for time series regression tasks, its performance in classification is unsatisfactory. In this paper, we propose \textit{FIC-TSC}, a training framework for time series classification that leverages Fisher information as the constraint. We theoretically and empirically show this is an efficient and effective solution to guide the model converge toward flatter minima, which enhances its generalizability to distribution shifts. We rigorously evaluate our method on 30 UEA multivariate and 85 UCR univariate datasets. Our empirical results demonstrate the superiority of the proposed method over 14 recent state-of-the-art methods.

Related papers

• FreRA: A Frequency-Refined Augmentation for Contrastive Learning on Time Series Classification [56.925103708982164]
  We present a novel perspective from the frequency domain and identify three advantages for downstream classification: global, independent, and compact.<n>We propose the lightweight yet effective Frequency Refined Augmentation (FreRA) tailored for time series contrastive learning on classification tasks.<n>FreRA consistently outperforms ten leading baselines on time series classification, anomaly detection, and transfer learning tasks.
  arXiv  Detail & Related papers  (2025-05-29T07:18:28Z)
• AimTS: Augmented Series and Image Contrastive Learning for Time Series Classification [19.7216139977931]
  Time series classification (TSC) is an important task in time series analysis.<n>AimTS is a pre-training framework that learns generalizable representations from multi-source time series data.
  arXiv  Detail & Related papers  (2025-04-14T08:55:16Z)
• 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)<n>In addition, we extract class-specific prior information derived from supervised information to enrich the positional encoding.<n>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)
• Improving the Evaluation and Actionability of Explanation Methods for Multivariate Time Series Classification [4.588028371034407]
  We focus on analyzing InterpretTime, a recent evaluation methodology for attribution methods applied to MTSC. We showcase some significant weaknesses of the original methodology and propose ideas to improve its accuracy and efficiency. We find that perturbation-based methods such as SHAP and Feature Ablation work well across a set of datasets.
  arXiv  Detail & Related papers  (2024-06-18T11:18:46Z)
• MISS: Multiclass Interpretable Scoring Systems [13.902264070785986]
  We present a machine-learning approach for constructing Multiclass Interpretable Scoring Systems (MISS) MISS is a fully data-driven methodology for single, sparse, and user-friendly scoring systems for multiclass classification problems. Results indicate that our approach is competitive with other machine learning models in terms of classification performance metrics and provides well-calibrated class probabilities.
  arXiv  Detail & Related papers  (2024-01-10T10:57:12Z)
• Multivariate Time Series Early Classification Across Channel and Time Dimensions [3.5786621294068373]
  We propose a more flexible early classification pipeline that offers a more granular consideration of input channels. Our method can enhance the early classification paradigm by achieving improved accuracy for equal input utilization.
  arXiv  Detail & Related papers  (2023-06-26T11:30:33Z)
• Mitigating Catastrophic Forgetting in Task-Incremental Continual Learning with Adaptive Classification Criterion [50.03041373044267]
  We propose a Supervised Contrastive learning framework with adaptive classification criterion for Continual Learning. Experiments show that CFL achieves state-of-the-art performance and has a stronger ability to overcome compared with the classification baselines.
  arXiv  Detail & Related papers  (2023-05-20T19:22:40Z)
• On the Trade-off of Intra-/Inter-class Diversity for Supervised Pre-training [72.8087629914444]
  We study the impact of the trade-off between the intra-class diversity (the number of samples per class) and the inter-class diversity (the number of classes) of a supervised pre-training dataset. With the size of the pre-training dataset fixed, the best downstream performance comes with a balance on the intra-/inter-class diversity.
  arXiv  Detail & Related papers  (2023-05-20T16:23:50Z)
• Real-Time Evaluation in Online Continual Learning: A New Hope [104.53052316526546]
  We evaluate current Continual Learning (CL) methods with respect to their computational costs. A simple baseline outperforms state-of-the-art CL methods under this evaluation. This surprisingly suggests that the majority of existing CL literature is tailored to a specific class of streams that is not practical.
  arXiv  Detail & Related papers  (2023-02-02T12:21:10Z)
• Systematic Evaluation of Predictive Fairness [60.0947291284978]
  Mitigating bias in training on biased datasets is an important open problem. We examine the performance of various debiasing methods across multiple tasks. We find that data conditions have a strong influence on relative model performance.
  arXiv  Detail & Related papers  (2022-10-17T05:40:13Z)
• No Fear of Heterogeneity: Classifier Calibration for Federated Learning with Non-IID Data [78.69828864672978]
  A central challenge in training classification models in the real-world federated system is learning with non-IID data. We propose a novel and simple algorithm called Virtual Representations (CCVR), which adjusts the classifier using virtual representations sampled from an approximated ssian mixture model. Experimental results demonstrate that CCVR state-of-the-art performance on popular federated learning benchmarks including CIFAR-10, CIFAR-100, and CINIC-10.
  arXiv  Detail & Related papers  (2021-06-09T12:02:29Z)

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.