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- W4386932930 abstract "Missing data is a commonly encountered problems in time series analysis, impeding accurate data analysis. Various methods have been proposed to impute missing values, including statistical, machine learning, and deep learning approaches. However, these methods either involve multi-steps, neglect temporal information, or are incapable of imputing missing data at desired time points. To overcome these limitations, this paper proposes a novel generative framework for imputing missing data, named the Augmented Neural Ordinary Differential Equation-assisted Generative Adversarial Network (ANODE-GAN). ANODE-GAN utilizes a Variational AutoEncoder (VAE) module to maps an incomplete time series instance to fixed-dimension initial latent vectors, generates continuous-time latent dynamics, and finally decodes them into complete data. With the aid of an additional discriminative network, ANODE-GAN can produce complete data that is closest to the original time series according to the squared error loss. By combining the generator and discriminator, ANODE-GAN is capable of imputing missing data at any desired time point while preserving the original feature distributions and temporal dynamics. Moreover, ANODE-GAN is evaluated on real-world datasets with varying missing rates by conducting the imputation task. A set of rigorous experiments show ANODE-GAN outperforms baseline methods in terms of Mean Square Error (MSE)." @default.
- W4386932930 created "2023-09-22" @default.
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- W4386932930 date "2023-01-01" @default.
- W4386932930 modified "2023-10-16" @default.
- W4386932930 title "ANODE-GAN: Incomplete Time Series Imputation by Augmented Neural ODE-Based Generative Adversarial Networks" @default.
- W4386932930 cites W2019126302 @default.
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- W4386932930 doi "https://doi.org/10.1007/978-3-031-44192-9_2" @default.
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