737 research outputs found
STING: Self-attention based Time-series Imputation Networks using GAN
Time series data are ubiquitous in real-world applications. However, one of
the most common problems is that the time series data could have missing values
by the inherent nature of the data collection process. So imputing missing
values from multivariate (correlated) time series data is imperative to improve
a prediction performance while making an accurate data-driven decision.
Conventional works for imputation simply delete missing values or fill them
based on mean/zero. Although recent works based on deep neural networks have
shown remarkable results, they still have a limitation to capture the complex
generation process of the multivariate time series. In this paper, we propose a
novel imputation method for multivariate time series data, called STING
(Self-attention based Time-series Imputation Networks using GAN). We take
advantage of generative adversarial networks and bidirectional recurrent neural
networks to learn latent representations of the time series. In addition, we
introduce a novel attention mechanism to capture the weighted correlations of
the whole sequence and avoid potential bias brought by unrelated ones.
Experimental results on three real-world datasets demonstrate that STING
outperforms the existing state-of-the-art methods in terms of imputation
accuracy as well as downstream tasks with the imputed values therein.Comment: 10 pages. This paper is an accepted version by ICDM'21. The published
version is https://ieeexplore.ieee.org/abstract/document/967918
Joint Modeling of Local and Global Temporal Dynamics for Multivariate Time Series Forecasting with Missing Values
Multivariate time series (MTS) forecasting is widely used in various domains,
such as meteorology and traffic. Due to limitations on data collection,
transmission, and storage, real-world MTS data usually contains missing values,
making it infeasible to apply existing MTS forecasting models such as linear
regression and recurrent neural networks. Though many efforts have been devoted
to this problem, most of them solely rely on local dependencies for imputing
missing values, which ignores global temporal dynamics. Local
dependencies/patterns would become less useful when the missing ratio is high,
or the data have consecutive missing values; while exploring global patterns
can alleviate such problems. Thus, jointly modeling local and global temporal
dynamics is very promising for MTS forecasting with missing values. However,
work in this direction is rather limited. Therefore, we study a novel problem
of MTS forecasting with missing values by jointly exploring local and global
temporal dynamics. We propose a new framework LGnet, which leverages memory
network to explore global patterns given estimations from local perspectives.
We further introduce adversarial training to enhance the modeling of global
temporal distribution. Experimental results on real-world datasets show the
effectiveness of LGnet for MTS forecasting with missing values and its
robustness under various missing ratios.Comment: Accepted by AAAI 202
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