Instruct: Identify the main and secondary category of Arxiv papers based on the titles and abstracts
Query: Time-series Imputation and Prediction with Bi-Directional Generative
  Adversarial Networks   Multivariate time-series data are used in many classification and regression
predictive tasks, and recurrent models have been widely used for such tasks.
Most common recurrent models assume that time-series data elements are of equal
length and the ordered observations are recorded at regular intervals. However,
real-world time-series data have neither a similar length nor a same number of
observations. They also have missing entries, which hinders the performance of
predictive tasks. In this paper, we approach these issues by presenting a model
for the combined task of imputing and predicting values for the irregularly
observed and varying length time-series data with missing entries. Our proposed
model (Bi-GAN) uses a bidirectional recurrent network in a generative
adversarial setting. The generator is a bidirectional recurrent network that
receives actual incomplete data and imputes the missing values. The
discriminator attempts to discriminate between the actual and the imputed
values in the output of the generator. Our model learns how to impute missing
elements in-between (imputation) or outside of the input time steps
(prediction), hence working as an effective any-time prediction tool for
time-series data. Our method has three advantages to the state-of-the-art
methods in the field: (a) single model can be used for both imputation and
prediction tasks; (b) it can perform prediction task for time-series of varying
length with missing data; (c) it does not require to know the observation and
prediction time window during training which provides a flexible length of
prediction window for both long-term and short-term predictions. We evaluate
our model on two public datasets and on another large real-world electronic
health records dataset to impute and predict body mass index (BMI) values in
children and show its superior performance in both settings.

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The categories of this paper are: hep-ph
32
The categories of this paper are: atom-ph
30
The categories of this paper are: q-alg
2
The categories of this paper are: math
18
The categories of this paper are: econ
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The categories of this paper are: acc-phys
37
The categories of this paper are: supr-con
6
The categories of this paper are: astro-ph
35
The categories of this paper are: mtrl-th
8
The categories of this paper are: hep-ex
14
The categories of this paper are: math-ph
21
The categories of this paper are: adap-org
28
The categories of this paper are: alg-geom
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The categories of this paper are: hep-lat
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The categories of this paper are: cond-mat
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The categories of this paper are: patt-sol