Filling out the missing gaps: Time Series Imputation with Semi-Supervised Learning

• URL: http://arxiv.org/abs/2304.04275v1
• Date: Sun, 9 Apr 2023 16:38:47 GMT
• Title: Filling out the missing gaps: Time Series Imputation with Semi-Supervised Learning
• Authors: Karan Aggarwal, Jaideep Srivastava
• Abstract summary: We propose a semi-supervised imputation method, ST-Impute, that uses both unlabeled data along with downstream task's labeled data. ST-Impute is based on sparse self-attention and trains on tasks that mimic the imputation process.
• Score: 7.8379910349669
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: Missing data in time series is a challenging issue affecting time series analysis. Missing data occurs due to problems like data drops or sensor malfunctioning. Imputation methods are used to fill in these values, with quality of imputation having a significant impact on downstream tasks like classification. In this work, we propose a semi-supervised imputation method, ST-Impute, that uses both unlabeled data along with downstream task's labeled data. ST-Impute is based on sparse self-attention and trains on tasks that mimic the imputation process. Our results indicate that the proposed method outperforms the existing supervised and unsupervised time series imputation methods measured on the imputation quality as well as on the downstream tasks ingesting imputed time series.

Related papers

• Task-oriented Time Series Imputation Evaluation via Generalized Representers [23.53722963890861]
  Time series analysis is widely used in many fields such as power energy, economics, and transportation. This paper proposes an efficient downstream task-oriented time series imputation evaluation approach.
  arXiv  Detail & Related papers  (2024-10-09T08:04:48Z)
• OML-AD: Online Machine Learning for Anomaly Detection in Time Series Data [0.0]
  We propose OML-AD, a novel approach for anomaly detection based on online machine learning (OML) We show that OML-AD outperforms state-of-the-art baseline methods in terms of accuracy and computational efficiency.
  arXiv  Detail & Related papers  (2024-09-15T14:19:19Z)
• Incremental Self-training for Semi-supervised Learning [56.57057576885672]
  IST is simple yet effective and fits existing self-training-based semi-supervised learning methods. We verify the proposed IST on five datasets and two types of backbone, effectively improving the recognition accuracy and learning speed.
  arXiv  Detail & Related papers  (2024-04-14T05:02:00Z)
• Deep Learning for Multivariate Time Series Imputation: A Survey [36.72913706617057]
  In this paper, we conduct a comprehensive survey on the recently proposed deep learning imputation methods. We propose a taxonomy for the reviewed methods, and then provide a structured review of these methods by highlighting their strengths and limitations. We also conduct empirical experiments to study different methods and compare their enhancement for downstream tasks.
  arXiv  Detail & Related papers  (2024-02-06T15:03:53Z)
• Graph Spatiotemporal Process for Multivariate Time Series Anomaly Detection with Missing Values [67.76168547245237]
  We introduce a novel framework called GST-Pro, which utilizes a graphtemporal process and anomaly scorer to detect anomalies. Our experimental results show that the GST-Pro method can effectively detect anomalies in time series data and outperforms state-of-the-art methods.
  arXiv  Detail & Related papers  (2024-01-11T10:10:16Z)
• Data-CUBE: Data Curriculum for Instruction-based Sentence Representation Learning [85.66907881270785]
  We propose a data curriculum method, namely Data-CUBE, that arranges the orders of all the multi-task data for training. In the task level, we aim to find the optimal task order to minimize the total cross-task interference risk. In the instance level, we measure the difficulty of all instances per task, then divide them into the easy-to-difficult mini-batches for training.
  arXiv  Detail & Related papers  (2024-01-07T18:12:20Z)
• DynImp: Dynamic Imputation for Wearable Sensing Data Through Sensory and Temporal Relatedness [78.98998551326812]
  We argue that traditional methods have rarely made use of both times-series dynamics of the data as well as the relatedness of the features from different sensors. We propose a model, termed as DynImp, to handle different time point's missingness with nearest neighbors along feature axis. We show that the method can exploit the multi-modality features from related sensors and also learn from history time-series dynamics to reconstruct the data under extreme missingness.
  arXiv  Detail & Related papers  (2022-09-26T21:59:14Z)
• STING: Self-attention based Time-series Imputation Networks using GAN [4.052758394413726]
  STING (Self-attention based Time-series Imputation Networks using GAN) is proposed. We take advantage of generative adversarial networks and bidirectional recurrent neural networks to learn latent representations of the time series. Experimental results on three real-world datasets demonstrate that STING outperforms the existing state-of-the-art methods in terms of imputation accuracy.
  arXiv  Detail & Related papers  (2022-09-22T06:06:56Z)
• Imputation-Free Learning from Incomplete Observations [73.15386629370111]
  We introduce the importance of guided gradient descent (IGSGD) method to train inference from inputs containing missing values without imputation. We employ reinforcement learning (RL) to adjust the gradients used to train the models via back-propagation. Our imputation-free predictions outperform the traditional two-step imputation-based predictions using state-of-the-art imputation methods.
  arXiv  Detail & Related papers  (2021-07-05T12:44:39Z)
• Time-Series Imputation with Wasserstein Interpolation for Optimal Look-Ahead-Bias and Variance Tradeoff [66.59869239999459]
  In finance, imputation of missing returns may be applied prior to training a portfolio optimization model. There is an inherent trade-off between the look-ahead-bias of using the full data set for imputation and the larger variance in the imputation from using only the training data. We propose a Bayesian posterior consensus distribution which optimally controls the variance and look-ahead-bias trade-off in the imputation.
  arXiv  Detail & Related papers  (2021-02-25T09:05:35Z)
• Time Series Data Imputation: A Survey on Deep Learning Approaches [4.4458738910060775]
  Time series data imputation is a well-studied problem with different categories of methods. Time series methods based on deep learning have made progress with the usage of models like RNN. We will review and discuss their model architectures, their pros and cons as well as their effects to show the development of the time series imputation methods.
  arXiv  Detail & Related papers  (2020-11-23T11:57:27Z)

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.