CoSTI: Consistency Models for (a faster) Spatio-Temporal Imputation

• URL: http://arxiv.org/abs/2501.19364v1
• Date: Fri, 31 Jan 2025 18:14:28 GMT
• Title: CoSTI: Consistency Models for (a faster) Spatio-Temporal Imputation
• Authors: Javier Solís-García, Belén Vega-Márquez, Juan A. Nepomuceno, Isabel A. Nepomuceno-Chamorro,
• Abstract summary: CoSTI employs Consistency Training to achieve comparable imputation quality to DDPMs while drastically reducing inference times. We evaluate CoSTI across multiple datasets and missing data scenarios, demonstrating up to a 98% reduction in imputation time with performance par with diffusion-based models.
• Score: 0.0
• License:
• Abstract: Multivariate Time Series Imputation (MTSI) is crucial for many applications, such as healthcare monitoring and traffic management, where incomplete data can compromise decision-making. Existing state-of-the-art methods, like Denoising Diffusion Probabilistic Models (DDPMs), achieve high imputation accuracy; however, they suffer from significant computational costs and are notably time-consuming due to their iterative nature. In this work, we propose CoSTI, an innovative adaptation of Consistency Models (CMs) for the MTSI domain. CoSTI employs Consistency Training to achieve comparable imputation quality to DDPMs while drastically reducing inference times, making it more suitable for real-time applications. We evaluate CoSTI across multiple datasets and missing data scenarios, demonstrating up to a 98% reduction in imputation time with performance on par with diffusion-based models. This work bridges the gap between efficiency and accuracy in generative imputation tasks, providing a scalable solution for handling missing data in critical spatio-temporal systems.

Related papers

• Scalable Numerical Embeddings for Multivariate Time Series: Enhancing Healthcare Data Representation Learning [6.635084843592727]
  We propose SCAlable Numerical Embedding (SCANE), a novel framework that treats each feature value as an independent token. SCANE regularizes the traits of distinct feature embeddings and enhances representational learning through a scalable embedding mechanism. We develop the nUMerical eMbeddIng Transformer (SUMMIT), which is engineered to deliver precise predictive outputs for MTS characterized by prevalent missing entries.
  arXiv  Detail & Related papers  (2024-05-26T13:06:45Z)
• Combating Missing Modalities in Egocentric Videos at Test Time [92.38662956154256]
  Real-world applications often face challenges with incomplete modalities due to privacy concerns, efficiency needs, or hardware issues. We propose a novel approach to address this issue at test time without requiring retraining. MiDl represents the first self-supervised, online solution for handling missing modalities exclusively at test time.
  arXiv  Detail & Related papers  (2024-04-23T16:01:33Z)
• Uncertainty-Aware Deep Attention Recurrent Neural Network for Heterogeneous Time Series Imputation [0.25112747242081457]
  Missingness is ubiquitous in multivariate time series and poses an obstacle to reliable downstream analysis. We propose DEep Attention Recurrent Imputation (Imputation), which jointly estimates missing values and their associated uncertainty. Experiments show that I surpasses the SOTA in diverse imputation tasks using real-world datasets.
  arXiv  Detail & Related papers  (2024-01-04T13:21:11Z)
• Deep Ensembles Meets Quantile Regression: Uncertainty-aware Imputation for Time Series [45.76310830281876]
  We propose Quantile Sub-Ensembles, a novel method to estimate uncertainty with ensemble of quantile-regression-based task networks. Our method not only produces accurate imputations that is robust to high missing rates, but also is computationally efficient due to the fast training of its non-generative model.
  arXiv  Detail & Related papers  (2023-12-03T05:52:30Z)
• PREM: A Simple Yet Effective Approach for Node-Level Graph Anomaly Detection [65.24854366973794]
  Node-level graph anomaly detection (GAD) plays a critical role in identifying anomalous nodes from graph-structured data in domains such as medicine, social networks, and e-commerce. We introduce a simple method termed PREprocessing and Matching (PREM for short) to improve the efficiency of GAD. Our approach streamlines GAD, reducing time and memory consumption while maintaining powerful anomaly detection capabilities.
  arXiv  Detail & Related papers  (2023-10-18T02:59:57Z)
• Perceiver-based CDF Modeling for Time Series Forecasting [25.26713741799865]
  We propose a new architecture, called perceiver-CDF, for modeling cumulative distribution functions (CDF) of time series data. Our approach combines the perceiver architecture with a copula-based attention mechanism tailored for multimodal time series prediction. Experiments on the unimodal and multimodal benchmarks consistently demonstrate a 20% improvement over state-of-the-art methods.
  arXiv  Detail & Related papers  (2023-10-03T01:13:17Z)
• Differentiable and Scalable Generative Adversarial Models for Data Imputation [24.111493826345082]
  SCIS consists of two modules, differentiable imputation modeling (DIM) and sample size estimation (SSE) Experiments upon several real-life large-scale datasets demonstrate that, our proposed system can accelerate the generative adversarial model training by 7.1x.
  arXiv  Detail & Related papers  (2022-01-10T08:03:14Z)
• MIRACLE: Causally-Aware Imputation via Learning Missing Data Mechanisms [82.90843777097606]
  We propose a causally-aware imputation algorithm (MIRACLE) for missing data. MIRACLE iteratively refines the imputation of a baseline by simultaneously modeling the missingness generating mechanism. We conduct extensive experiments on synthetic and a variety of publicly available datasets to show that MIRACLE is able to consistently improve imputation.
  arXiv  Detail & Related papers  (2021-11-04T22:38:18Z)
• CSDI: Conditional Score-based Diffusion Models for Probabilistic Time Series Imputation [107.63407690972139]
  Conditional Score-based Diffusion models for Imputation (CSDI) is a novel time series imputation method that utilizes score-based diffusion models conditioned on observed data. CSDI improves by 40-70% over existing probabilistic imputation methods on popular performance metrics. In addition, C reduces the error by 5-20% compared to the state-of-the-art deterministic imputation methods.
  arXiv  Detail & Related papers  (2021-07-07T22:20:24Z)
• Closed-form Continuous-Depth Models [99.40335716948101]
  Continuous-depth neural models rely on advanced numerical differential equation solvers. We present a new family of models, termed Closed-form Continuous-depth (CfC) networks, that are simple to describe and at least one order of magnitude faster.
  arXiv  Detail & Related papers  (2021-06-25T22:08:51Z)
• Improving a State-of-the-Art Heuristic for the Minimum Latency Problem with Data Mining [69.00394670035747]
  Hybrid metaheuristics have become a trend in operations research. A successful example combines the Greedy Randomized Adaptive Search Procedures (GRASP) and data mining techniques.
  arXiv  Detail & Related papers  (2019-08-28T13:12:30Z)

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.