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We focus on solving the univariate times series point forecasting problem using deep learning.
Forecasting trends and seasonals by exponentially weighted averages
C. C. Holt · 1957
Earlier work this paper cites.
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Peter R. Winters · 1960
Earlier work this paper cites.
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Spyros Makridakis, A Andersen, Robert Carbone, Robert Fildes, Michele Hibon, Rudolf Lewandowski, Joseph Newton, Emanuel Parzen, and Robert Winkler · 1982
Earlier work this paper cites.
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Earlier work this paper cites.
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Earlier work this paper cites.
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Earlier work this paper cites.
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Earlier work this paper cites.
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Earlier work this paper cites.
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Earlier work this paper cites.
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Earlier work this paper cites.
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Earlier work this paper cites.
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