Online Conformal Model Selection for Nonstationary Time Series

• URL: http://arxiv.org/abs/2506.05544v1
• Date: Thu, 05 Jun 2025 19:45:52 GMT
• Title: Online Conformal Model Selection for Nonstationary Time Series
• Authors: Shibo Li, Yao Zheng,
• Abstract summary: MPS (Model Prediction Set) is a novel framework for online model selection for nonstationary time series.<n>We show that MPS reliably and efficiently identifies optimal models under nonstationarity.<n>As a generic framework, MPS accommodates any data-generating process, data structure, model class, training method, and evaluation metric.
• Score: 7.404114840043908
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: This paper introduces the MPS (Model Prediction Set), a novel framework for online model selection for nonstationary time series. Classical model selection methods, such as information criteria and cross-validation, rely heavily on the stationarity assumption and often fail in dynamic environments which undergo gradual or abrupt changes over time. Yet real-world data are rarely stationary, and model selection under nonstationarity remains a largely open problem. To tackle this challenge, we combine conformal inference with model confidence sets to develop a procedure that adaptively selects models best suited to the evolving dynamics at any given time. Concretely, the MPS updates in real time a confidence set of candidate models that covers the best model for the next time period with a specified long-run probability, while adapting to nonstationarity of unknown forms. Through simulations and real-world data analysis, we demonstrate that MPS reliably and efficiently identifies optimal models under nonstationarity, an essential capability lacking in offline methods. Moreover, MPS frequently produces high-quality sets with small cardinality, whose evolution offers deeper insights into changing dynamics. As a generic framework, MPS accommodates any data-generating process, data structure, model class, training method, and evaluation metric, making it broadly applicable across diverse problem settings.

Related papers

• Enhancing Online Continual Learning with Plug-and-Play State Space Model and Class-Conditional Mixture of Discretization [72.81319836138347]
  Online continual learning (OCL) seeks to learn new tasks from data streams that appear only once, while retaining knowledge of previously learned tasks.<n>Most existing methods rely on replay, focusing on enhancing memory retention through regularization or distillation.<n>We introduce a plug-and-play module, S6MOD, which can be integrated into most existing methods and directly improve adaptability.
  arXiv  Detail & Related papers  (2024-12-24T05:25:21Z)
• Recurrent Neural Goodness-of-Fit Test for Time Series [8.22915954499148]
  Time series data are crucial across diverse domains such as finance and healthcare.<n>Traditional evaluation metrics fall short due to the temporal dependencies and potential high dimensionality of the features.<n>We propose the REcurrent NeurAL (RENAL) Goodness-of-Fit test, a novel and statistically rigorous framework for evaluating generative time series models.
  arXiv  Detail & Related papers  (2024-10-17T19:32:25Z)
• MITA: Bridging the Gap between Model and Data for Test-time Adaptation [68.62509948690698]
  Test-Time Adaptation (TTA) has emerged as a promising paradigm for enhancing the generalizability of models. We propose Meet-In-The-Middle based MITA, which introduces energy-based optimization to encourage mutual adaptation of the model and data from opposing directions.
  arXiv  Detail & Related papers  (2024-10-12T07:02:33Z)
• MOTO: Offline Pre-training to Online Fine-tuning for Model-based Robot Learning [52.101643259906915]
  We study the problem of offline pre-training and online fine-tuning for reinforcement learning from high-dimensional observations. Existing model-based offline RL methods are not suitable for offline-to-online fine-tuning in high-dimensional domains. We propose an on-policy model-based method that can efficiently reuse prior data through model-based value expansion and policy regularization.
  arXiv  Detail & Related papers  (2024-01-06T21:04:31Z)
• 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)
• Data-driven Preference Learning Methods for Sorting Problems with Multiple Temporal Criteria [17.673512636899076]
  This study presents novel preference learning approaches to multiple criteria sorting problems in the presence of temporal criteria. To enhance scalability and accommodate learnable time discount factors, we introduce a novel monotonic Recurrent Neural Network (mRNN) The proposed mRNN can describe the preference dynamics by depicting marginal value functions and personalized time discount factors along with time.
  arXiv  Detail & Related papers  (2023-09-22T05:08:52Z)
• Time Series Continuous Modeling for Imputation and Forecasting with Implicit Neural Representations [15.797295258800638]
  We introduce a novel modeling approach for time series imputation and forecasting, tailored to address the challenges often encountered in real-world data. Our method relies on a continuous-time-dependent model of the series' evolution dynamics. A modulation mechanism, driven by a meta-learning algorithm, allows adaptation to unseen samples and extrapolation beyond observed time-windows.
  arXiv  Detail & Related papers  (2023-06-09T13:20:04Z)
• Revisiting Design Choices in Model-Based Offline Reinforcement Learning [39.01805509055988]
  Offline reinforcement learning enables agents to leverage large pre-collected datasets of environment transitions to learn control policies. This paper compares and designs novel protocols to investigate their interaction with other hyper parameters, such as the number of models, or imaginary rollout horizon.
  arXiv  Detail & Related papers  (2021-10-08T13:51:34Z)
• Autoregressive Dynamics Models for Offline Policy Evaluation and Optimization [60.73540999409032]
  We show that expressive autoregressive dynamics models generate different dimensions of the next state and reward sequentially conditioned on previous dimensions. We also show that autoregressive dynamics models are useful for offline policy optimization by serving as a way to enrich the replay buffer.
  arXiv  Detail & Related papers  (2021-04-28T16:48:44Z)
• Anomaly Detection of Time Series with Smoothness-Inducing Sequential Variational Auto-Encoder [59.69303945834122]
  We present a Smoothness-Inducing Sequential Variational Auto-Encoder (SISVAE) model for robust estimation and anomaly detection of time series. Our model parameterizes mean and variance for each time-stamp with flexible neural networks. We show the effectiveness of our model on both synthetic datasets and public real-world benchmarks.
  arXiv  Detail & Related papers  (2021-02-02T06:15:15Z)
• Reinforcement Learning based dynamic weighing of Ensemble Models for Time Series Forecasting [0.8399688944263843]
  It is known that if models selected for data modelling are distinct (linear/non-linear, static/dynamic) and independent (minimally correlated) models, the accuracy of the predictions is improved. Various approaches suggested in the literature to weigh the ensemble models use a static set of weights. To address this issue, a Reinforcement Learning (RL) approach to dynamically assign and update weights of each of the models at different time instants.
  arXiv  Detail & Related papers  (2020-08-20T10:40:42Z)

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.