Aggregation Algorithm vs. Average For Time Series Prediction

Waqas Jamil; Yury Kalnishkan; Hamid Bouchachia

Aggregation Algorithm vs. Average For Time Series Prediction

Waqas Jamil, Yury Kalnishkan, Hamid Bouchachia

Research output: Chapter in Book/Report/Conference proceeding › Conference contribution

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Abstract

Learning with expert advice as a scheme of on-line learning has been very successfully applied to various learning problems due to its strong theoretical basis. In this paper, for the purpose of times series prediction, we investigate the application of Aggregation Algorithm, which a generalisation of the famous weighted majority algorithm. The results of the experiments done, show that the Aggregation Algorithm performs very well in comparison to average.

Original language	English
Title of host publication	Proceedings of the ECML PKDD 2016 Workshop on Large-scale Learning from Data Streams in Evolving Environments, STREAMEVOLV-2016
Pages	1-14
Number of pages	14
Publication status	Published - 23 Sept 2016

Keywords

Aggregation Algorithm; time-series; auto-regressive-moving-average; auto-regressive-integrated-moving-average; Fourier transform; on-line learning

Access to Document

Accepted ManuscriptAccepted author manuscript, 1.58 MB

Cite this

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title = "Aggregation Algorithm vs. Average For Time Series Prediction",

abstract = "Learning with expert advice as a scheme of on-line learning has been very successfully applied to various learning problems due to its strong theoretical basis. In this paper, for the purpose of times series prediction, we investigate the application of Aggregation Algorithm, which a generalisation of the famous weighted majority algorithm. The results of the experiments done, show that the Aggregation Algorithm performs very well in comparison to average.",

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year = "2016",

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AU - Bouchachia, Hamid

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N2 - Learning with expert advice as a scheme of on-line learning has been very successfully applied to various learning problems due to its strong theoretical basis. In this paper, for the purpose of times series prediction, we investigate the application of Aggregation Algorithm, which a generalisation of the famous weighted majority algorithm. The results of the experiments done, show that the Aggregation Algorithm performs very well in comparison to average.

AB - Learning with expert advice as a scheme of on-line learning has been very successfully applied to various learning problems due to its strong theoretical basis. In this paper, for the purpose of times series prediction, we investigate the application of Aggregation Algorithm, which a generalisation of the famous weighted majority algorithm. The results of the experiments done, show that the Aggregation Algorithm performs very well in comparison to average.

KW - Aggregation Algorithm; time-series; auto-regressive-moving-average; auto-regressive-integrated-moving-average; Fourier transform; on-line learning

M3 - Conference contribution

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BT - Proceedings of the ECML PKDD 2016 Workshop on Large-scale Learning from Data Streams in Evolving Environments, STREAMEVOLV-2016

ER -