GLIMA: Global and Local Time Series Imputation with Multi-directional Attention Learning

Suo, Qiuling; Zhong, Wenhui; Xun, Guangxu; Sun, Jianhui; Chen, Changyou; Zhang, Aidong

doi:10.1109/bigdata50022.2020.9378408

Cited by 9 publications

(8 citation statements)

References 17 publications

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“…We demonstrate that CSDI also provides accurate deterministic imputations, which are obtained as the median of 100 generated samples. We compare CSDI with four baselines developed for deterministic imputation including GLIMA [20], which combined recurrent imputations with an attention mechanism to capture temporal and feature dependencies and showed the state-of-the-art performance. These methods are based on autoregressive models.…”

Section: Results Of Deterministic Imputationmentioning

confidence: 99%

“…Subsequent studies combined RNNs with other methods to improve imputation performance, such as GANs [9,17,18] and self-training [19]. Among them, the combination of RNNs with attention mechanisms is particularly successful for imputation and interpolation of time series [20,21]. While these methods focused on deterministic imputation, GP-VAE [10] has been recently developed as a probabilistic imputation method.…”

Section: Related Workmentioning

confidence: 99%

“…The second one is the air quality dataset [2]. Following previous studies [7,20], we use hourly sampled PM2.5 measurements from 36 stations in Beijing for 12 months and set 36 consecutive time steps as one time series. There are around 13% missing values and the missing patterns are not random.…”

Section: Time Series Imputationmentioning

confidence: 99%

“…2) V-RIN [30]: the method utilizes the uncertainty learned with VAE to improve recurrent imputation. 3) GLIMA [20]: the method combines recurrent imputations with an attention mechanism to capture cross-time and cross-feature dependencies and shows the state-of-the-art performance. 4) RDIS [19]: the method applies random drops to given training data for self-training.…”

Section: E2 Details Of Experiments Settings In Sectionmentioning

confidence: 99%

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CSDI: Conditional Score-based Diffusion Models for Probabilistic Time Series Imputation

Tashiro¹,

Song²,

Song³

et al. 2021

Preprint

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The imputation of missing values in time series has many applications in healthcare and finance. While autoregressive models are natural candidates for time series imputation, score-based diffusion models have recently outperformed existing counterparts including autoregressive models in many tasks such as image generation and audio synthesis, and would be promising for time series imputation. In this paper, we propose Conditional Score-based Diffusion models for Imputation (CSDI), a novel time series imputation method that utilizes score-based diffusion models conditioned on observed data. Unlike existing score-based approaches, the conditional diffusion model is explicitly trained for imputation and can exploit correlations between observed values. On healthcare and environmental data, CSDI improves by 40-70% over existing probabilistic imputation methods on popular performance metrics. In addition, deterministic imputation by CSDI reduces the error by 5-20% compared to the state-of-the-art deterministic imputation methods. Furthermore, CSDI can also be applied to time series interpolation and probabilistic forecasting, and is competitive with existing baselines.

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Section: Results Of Deterministic Imputationmentioning

confidence: 99%

Section: Related Workmentioning

confidence: 99%

Section: Time Series Imputationmentioning

confidence: 99%

Section: E2 Details Of Experiments Settings In Sectionmentioning

confidence: 99%

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CSDI: Conditional Score-based Diffusion Models for Probabilistic Time Series Imputation

Tashiro¹,

Song²,

Song³

et al. 2021

Preprint

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“…Despite the prevalence of the self-attention mechanism in many domains, its application to time-series imputation is still being explored. Suo et al [32] propose an imputation framework that learns the distant correlations across time and the dependencies of multivariate time series locally and globally using a multi-dimensional attention mechanism. Du et al [33] propose the method of a self-attention-based mechanism for missing values in multivariate time series (SAITS).…”

Section: Time-series Imputation Via Neural Networkmentioning

confidence: 99%

Multivariate Time Series Imputation With Transformers

Yldz

Koc

Koç

2022

IEEE Signal Process. Lett.

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Processing time series with missing segments is a fundamental challenge that puts obstacles to advanced analysis in various disciplines such as engineering, medicine, and economics. One of the remedies is imputation to fill the missing values based on observed values properly without undermining performance. We propose the Multivariate Time-Series Imputation with Transformers (MTSIT), a novel method that uses transformer architecture in an unsupervised manner for missing value imputation. Unlike the existing transformer architectures, this model only uses the encoder part of the transformer due to computational benefits. Crucially, MTSIT trains the autoencoder by jointly reconstructing and imputing stochastically-masked inputs via an objective designed for multivariate time-series data. The trained autoencoder is then evaluated for imputing both simulated and real missing values. Experiments show that MTSIT outperforms state-of-the-art imputation methods over benchmark datasets.

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DAGAN:Generative Adversarial Network with Dual Attention-Enhanced GRU for Multivariate Time Series Imputation

Song,

Fang,

et al. 2023

Communications in Computer and Information Science

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GLIMA: Global and Local Time Series Imputation with Multi-directional Attention Learning

Cited by 9 publications

References 17 publications

CSDI: Conditional Score-based Diffusion Models for Probabilistic Time Series Imputation

CSDI: Conditional Score-based Diffusion Models for Probabilistic Time Series Imputation

Multivariate Time Series Imputation With Transformers

DAGAN:Generative Adversarial Network with Dual Attention-Enhanced GRU for Multivariate Time Series Imputation

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