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Deep Neural Networks for Irregular Astronomical Time Series Pavlov Prototypes Institute for Applied Computational Science, Harvard Collaborators: Abhishek Malawi and Ranger MaatOutline Motivation
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To fill out irregular astronomical time series, follow these steps:
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Collect the available data points of the time series.
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Determine the pattern and potential causes of irregularity in the time series.
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Regularly update and validate the filled time series to incorporate new data and improve accuracy over time.

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Irregular astronomical time series are needed by various individuals and organizations in astronomy and related fields, including:
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- Astronomical researchers: They analyze irregular time series to study the behavior and characteristics of astronomical phenomena.
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- Observatories: They use irregular time series to monitor and track the changes in celestial objects.
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- Universities and educational institutions: They utilize irregular time series to teach astronomy concepts and conduct research.
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- Meteorologists: They analyze irregular time series to study the impact of astronomical events on Earth's climate and weather patterns.

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