An Efficient Continual Learning Framework for Multivariate Time Series Prediction Tasks with Application to Vehicle State Estimation

• URL: http://arxiv.org/abs/2503.01669v2
• Date: Fri, 04 Apr 2025 21:40:47 GMT
• Title: An Efficient Continual Learning Framework for Multivariate Time Series Prediction Tasks with Application to Vehicle State Estimation
• Authors: Arvin Hosseinzadeh, Ladan Khoshnevisan, Mohammad Pirani, Shojaeddin Chenouri, Amir Khajepour,
• Abstract summary: We present EM-ReSeleCT, an enhanced approach to handle continual learning in time series tasks.<n>Our approach strategically selects representative subsets from old and historical data.<n>We also develop a sequence-to-sequence transformer model (autoregressive model) specifically designed for vehicle state estimation.
• Score: 11.197937792525684
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: In continual time series analysis using neural networks, catastrophic forgetting (CF) of previously learned models when training on new data domains has always been a significant challenge. This problem is especially challenging in vehicle estimation and control, where new information is sequentially introduced to the model. Unfortunately, existing work on continual learning has not sufficiently addressed the adverse effects of catastrophic forgetting in time series analysis, particularly in multivariate output environments. In this paper, we present EM-ReSeleCT (Efficient Multivariate Representative Selection for Continual Learning in Time Series Tasks), an enhanced approach designed to handle continual learning in multivariate environments. Our approach strategically selects representative subsets from old and historical data and incorporates memory-based continual learning techniques with an improved optimization algorithm to adapt the pre-trained model on new information while preserving previously acquired information. Additionally, we develop a sequence-to-sequence transformer model (autoregressive model) specifically designed for vehicle state estimation. Moreover, we propose an uncertainty quantification framework using conformal prediction to assess the sensitivity of the memory size and to showcase the robustness of the proposed method. Experimental results from tests on an electric Equinox vehicle highlight the superiority of our method in continually learning new information while retaining prior knowledge, outperforming state-of-the-art continual learning methods. Furthermore, EM-ReSeleCT significantly reduces training time, a critical advantage in continual learning applications.

Related papers

• Preserving Seasonal and Trend Information: A Variational Autoencoder-Latent Space Arithmetic Based Approach for Non-stationary Learning [0.0]
  This research proposes a method for enforcing stationary behaviour within the latent space while preserving trend and seasonal information. The approach's ability to preserve trend and seasonal information was evaluated on two time-series non-stationary datasets.
  arXiv  Detail & Related papers  (2025-04-26T06:29:06Z)
• Temporal-Difference Variational Continual Learning [89.32940051152782]
  A crucial capability of Machine Learning models in real-world applications is the ability to continuously learn new tasks. In Continual Learning settings, models often struggle to balance learning new tasks with retaining previous knowledge. We propose new learning objectives that integrate the regularization effects of multiple previous posterior estimations.
  arXiv  Detail & Related papers  (2024-10-10T10:58:41Z)
• Remember and Recall: Associative-Memory-based Trajectory Prediction [25.349986959111757]
  We propose the Fragmented-Memory-based Trajectory Prediction (FMTP) model, inspired by the remarkable learning capabilities of humans. The FMTP model employs discrete representations to enhance computational efficiency by reducing information redundancy. We develop an advanced reasoning engine based on language models to deeply learn the associative rules among these discrete representations.
  arXiv  Detail & Related papers  (2024-10-03T04:32:21Z)
• Learn while Unlearn: An Iterative Unlearning Framework for Generative Language Models [52.03511469562013]
  We introduce the Iterative Contrastive Unlearning (ICU) framework, which consists of three core components. A Knowledge Unlearning Induction module targets specific knowledge for removal using an unlearning loss. A Contrastive Learning Enhancement module preserves the model's expressive capabilities against the pure unlearning goal. An Iterative Unlearning Refinement module dynamically adjusts the unlearning process through ongoing evaluation and updates.
  arXiv  Detail & Related papers  (2024-07-25T07:09:35Z)
• Temporal Knowledge Distillation for Time-Sensitive Financial Services Applications [7.1795069620810805]
  Anomaly detection is frequently used in key compliance and risk functions such as financial crime detection fraud and cybersecurity. Keeping up with the rapid changes by retraining the models with the latest data patterns introduces pressures in balancing the historical and current patterns. The proposed approach provides advantages in retraining times while improving the model performance.
  arXiv  Detail & Related papers  (2023-12-28T03:04:30Z)
• Towards Robust Continual Learning with Bayesian Adaptive Moment Regularization [51.34904967046097]
  Continual learning seeks to overcome the challenge of catastrophic forgetting, where a model forgets previously learnt information. We introduce a novel prior-based method that better constrains parameter growth, reducing catastrophic forgetting. Results show that BAdam achieves state-of-the-art performance for prior-based methods on challenging single-headed class-incremental experiments.
  arXiv  Detail & Related papers  (2023-09-15T17:10:51Z)
• PILOT: A Pre-Trained Model-Based Continual Learning Toolbox [65.57123249246358]
  This paper introduces a pre-trained model-based continual learning toolbox known as PILOT.<n>On the one hand, PILOT implements some state-of-the-art class-incremental learning algorithms based on pre-trained models, such as L2P, DualPrompt, and CODA-Prompt.<n>On the other hand, PILOT fits typical class-incremental learning algorithms within the context of pre-trained models to evaluate their effectiveness.
  arXiv  Detail & Related papers  (2023-09-13T17:55:11Z)
• Overcoming Generic Knowledge Loss with Selective Parameter Update [48.240683797965005]
  We propose a novel approach to continuously update foundation models. Instead of updating all parameters equally, we localize the updates to a sparse set of parameters relevant to the task being learned. Our method achieves improvements on the accuracy of the newly learned tasks up to 7% while preserving the pretraining knowledge with a negligible decrease of 0.9% on a representative control set accuracy.
  arXiv  Detail & Related papers  (2023-08-23T22:55:45Z)
• Continual Learning with Pretrained Backbones by Tuning in the Input Space [44.97953547553997]
  The intrinsic difficulty in adapting deep learning models to non-stationary environments limits the applicability of neural networks to real-world tasks. We propose a novel strategy to make the fine-tuning procedure more effective, by avoiding to update the pre-trained part of the network and learning not only the usual classification head, but also a set of newly-introduced learnable parameters.
  arXiv  Detail & Related papers  (2023-06-05T15:11:59Z)
• Effective and Efficient Training for Sequential Recommendation using Recency Sampling [91.02268704681124]
  We propose a novel Recency-based Sampling of Sequences training objective. We show that the models enhanced with our method can achieve performances exceeding or very close to stateof-the-art BERT4Rec.
  arXiv  Detail & Related papers  (2022-07-06T13:06:31Z)
• Active Learning for Sequence Tagging with Deep Pre-trained Models and Bayesian Uncertainty Estimates [52.164757178369804]
  Recent advances in transfer learning for natural language processing in conjunction with active learning open the possibility to significantly reduce the necessary annotation budget. We conduct an empirical study of various Bayesian uncertainty estimation methods and Monte Carlo dropout options for deep pre-trained models in the active learning framework. We also demonstrate that to acquire instances during active learning, a full-size Transformer can be substituted with a distilled version, which yields better computational performance.
  arXiv  Detail & Related papers  (2021-01-20T13:59:25Z)

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.