ContiFormer: Continuous-Time Transformer for Irregular Time Series Modeling

• URL: http://arxiv.org/abs/2402.10635v1
• Date: Fri, 16 Feb 2024 12:34:38 GMT
• Title: ContiFormer: Continuous-Time Transformer for Irregular Time Series Modeling
• Abstract summary: Modeling continuous-time dynamics on irregular time series is critical to account for data evolution and correlations that occur continuously. Traditional methods including recurrent neural networks or Transformer models leverage inductive bias via powerful neural architectures to capture complex patterns. We propose ContiFormer that extends the relation modeling of vanilla Transformer to the continuous-time domain.
• Score: 30.12824131306359
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: Modeling continuous-time dynamics on irregular time series is critical to account for data evolution and correlations that occur continuously. Traditional methods including recurrent neural networks or Transformer models leverage inductive bias via powerful neural architectures to capture complex patterns. However, due to their discrete characteristic, they have limitations in generalizing to continuous-time data paradigms. Though neural ordinary differential equations (Neural ODEs) and their variants have shown promising results in dealing with irregular time series, they often fail to capture the intricate correlations within these sequences. It is challenging yet demanding to concurrently model the relationship between input data points and capture the dynamic changes of the continuous-time system. To tackle this problem, we propose ContiFormer that extends the relation modeling of vanilla Transformer to the continuous-time domain, which explicitly incorporates the modeling abilities of continuous dynamics of Neural ODEs with the attention mechanism of Transformers. We mathematically characterize the expressive power of ContiFormer and illustrate that, by curated designs of function hypothesis, many Transformer variants specialized in irregular time series modeling can be covered as a special case of ContiFormer. A wide range of experiments on both synthetic and real-world datasets have illustrated the superior modeling capacities and prediction performance of ContiFormer on irregular time series data. The project link is https://seqml.github.io/contiformer/.

Related papers

• Generative Modeling of Approximately Periodic Time Series by a Posterior-Weighted Gaussian Process [0.4460583138505672]
  We propose a generative model for approximately periodic time series.<n>Our approach decouples intra-repetition structure from inter-repetition variability through a two-stage construction.
  arXiv  Detail & Related papers  (2026-05-13T08:17:00Z)
• Forecasting as Rendering: A 2D Gaussian Splatting Framework for Time Series Forecasting [79.37674445572462]
  Time series forecasting (TSF) remains a challenging problem due to the intricate entanglement of intraperiod-fluctuations and interperiod-trends.<n>Treating reshaped tensors as static images results in a topological mismatch.<n> relying on uniform fixed-size representations allocates modeling capacity inefficiently.<n>TimeGS is a novel framework that fundamentally shifts the forecasting paradigm from regression to 2D generative rendering.
  arXiv  Detail & Related papers  (2026-02-10T14:13:36Z)
• DiTS: Multimodal Diffusion Transformers Are Time Series Forecasters [50.43534351968113]
  Existing generative time series models do not address the multi-dimensional properties of time series data well.<n>Inspired by Multimodal Diffusion Transformers that integrate textual guidance into video generation, we propose Diffusion Transformers for Time Series (DiTS)
  arXiv  Detail & Related papers  (2026-02-06T10:48:13Z)
• Hydra: Dual Exponentiated Memory for Multivariate Time Series Analysis [9.59658906532313]
  We present Hydra, a by-design two-headed meta-in-context memory module.<n>It learns how to memorize patterns at test time by prioritizing time series patterns.<n>We also present a new 2D-chunkwise training algorithm that approximates the actual recurrence with $times 10$ efficiency improvement.
  arXiv  Detail & Related papers  (2025-11-02T16:03:59Z)
• Modeling Time Series Dynamics with Fourier Ordinary Differential Equations [5.156484100374059]
  We introduce FODEs, an approach that embeds the dynamics in the Fourier domain.<n>FODEs uncover global patterns and periodic behaviors that remain elusive in the time domain.<n> Experiments on various time series datasets demonstrate that FODEs outperform existing methods in terms of both accuracy and efficiency.
  arXiv  Detail & Related papers  (2025-10-05T10:27:45Z)
• Time Tracker: Mixture-of-Experts-Enhanced Foundation Time Series Forecasting Model with Decoupled Training Pipelines [5.543238821368548]
  Time series often exhibit significant diversity in their temporal patterns across different time spans and domains.<n>Time Tracker achieves state-of-the-art performance in predicting accuracy, model generalization and adaptability.
  arXiv  Detail & Related papers  (2025-05-21T06:18:41Z)
• Generative Models for Long Time Series: Approximately Equivariant Recurrent Network Structures for an Adjusted Training Scheme [4.327763441385371]
  We present a simple yet effective generative model for time series data based on a Variational Autoencoder (VAE) with recurrent layers.<n>Our method introduces an adapted training scheme that progressively increases the sequence length.<n>By leveraging the recurrent architecture, the model maintains a constant number of parameters regardless of sequence length.
  arXiv  Detail & Related papers  (2025-05-08T07:52:37Z)
• TimeDiT: General-purpose Diffusion Transformers for Time Series Foundation Model [11.281386703572842]
  A family of models have been developed, utilizing a temporal auto-regressive generative Transformer architecture. TimeDiT is a general foundation model for time series that employs a denoising diffusion paradigm instead of temporal auto-regressive generation. Extensive experiments conducted on a varity of tasks such as forecasting, imputation, and anomaly detection, demonstrate the effectiveness of TimeDiT.
  arXiv  Detail & Related papers  (2024-09-03T22:31:57Z)
• Rough Transformers: Lightweight Continuous-Time Sequence Modelling with Path Signatures [46.58170057001437]
  We introduce the Rough Transformer, a variation of the Transformer model that operates on continuous-time representations of input sequences. We find that, on a variety of time-series-related tasks, Rough Transformers consistently outperform their vanilla attention counterparts.
  arXiv  Detail & Related papers  (2024-05-31T14:00:44Z)
• Rough Transformers for Continuous and Efficient Time-Series Modelling [46.58170057001437]
  Time-series data in real-world medical settings typically exhibit long-range dependencies and are observed at non-uniform intervals. We introduce the Rough Transformer, a variation of the Transformer model which operates on continuous-time representations of input sequences. We find that Rough Transformers consistently outperform their vanilla attention counterparts while obtaining the benefits of Neural ODE-based models.
  arXiv  Detail & Related papers  (2024-03-15T13:29:45Z)
• A Poisson-Gamma Dynamic Factor Model with Time-Varying Transition Dynamics [51.147876395589925]
  A non-stationary PGDS is proposed to allow the underlying transition matrices to evolve over time. A fully-conjugate and efficient Gibbs sampler is developed to perform posterior simulation. Experiments show that, in comparison with related models, the proposed non-stationary PGDS achieves improved predictive performance.
  arXiv  Detail & Related papers  (2024-02-26T04:39:01Z)
• 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)
• Model-Attentive Ensemble Learning for Sequence Modeling [86.4785354333566]
  We present Model-Attentive Ensemble learning for Sequence modeling (MAES) MAES is a mixture of time-series experts which leverages an attention-based gating mechanism to specialize the experts on different sequence dynamics and adaptively weight their predictions. We demonstrate that MAES significantly out-performs popular sequence models on datasets subject to temporal shift.
  arXiv  Detail & Related papers  (2021-02-23T05:23:35Z)
• 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)
• Synergetic Learning of Heterogeneous Temporal Sequences for Multi-Horizon Probabilistic Forecasting [48.8617204809538]
  We propose Variational Synergetic Multi-Horizon Network (VSMHN), a novel deep conditional generative model. To learn complex correlations across heterogeneous sequences, a tailored encoder is devised to combine the advances in deep point processes models and variational recurrent neural networks. Our model can be trained effectively using variational inference and generates predictions with Monte-Carlo simulation.
  arXiv  Detail & Related papers  (2021-01-31T11:00:55Z)
• Liquid Time-constant Networks [117.57116214802504]
  We introduce a new class of time-continuous recurrent neural network models. Instead of declaring a learning system's dynamics by implicit nonlinearities, we construct networks of linear first-order dynamical systems. These neural networks exhibit stable and bounded behavior, yield superior expressivity within the family of neural ordinary differential equations.
  arXiv  Detail & Related papers  (2020-06-08T09:53:35Z)

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.