Adaptive Online Learning for Time Series Prediction
Keyword(s):
We study the problem of predicting time series data using the autoregressive integrated moving average (ARIMA) model in an online manner. Existing algorithms require model selection, which is time consuming and inapt for the setting of online learning. Using adaptive online learning techniques, we develop algorithms for fitting ARIMA models with fewest possible hyperparameters. We analyse the regret bound of the proposed algorithms and examine their performance using experiments on both synthetic and real world datasets
2019 ◽
Vol 13
(3)
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pp. 135-144
2020 ◽
Vol 2020
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pp. 1-11
Keyword(s):
2014 ◽
Vol 1
(1)
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pp. 841-876
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Keyword(s):
2015 ◽
Vol 1
(1)
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pp. 15
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Prediction Of Tiger Shrimp Supply Using Time Series Anlysis Method Case Study CV.Surya Perdana Benur
2021 ◽
Vol 3
(1)
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pp. 27-32
2017 ◽
Vol 4
(1)
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pp. 27
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