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Multi-resolution forecast aggregation for time series in agri datasets
Bahrpeyma, Fouad; Roantree, Mark; McCarren, Andrew
A wide variety of phenomena are characterized by time dependent dynamics that can be analyzed using time series methods. Various time series analysis techniques have been presented, each addressing certain aspects of the data. In time series analysis, forecasting is a challenging problem when attempting to estimate extended time horizons which effectively encapsulate multi-step-ahead (MSA) predictions. Two original solutions to MSA are the direct and the recursive approaches. Recent studies have mainly focused on combining previous methods as an attempt to overcome the problem of discarding sequential correlation in the direct strategy or accumulation of error in the recursive strategy. This paper introduces a technique known as Multi-Resolution Forecast Aggregation (MRFA) which incorporates an additional concept known as Resolutions of Impact. MRFA is shown to have favourable prediction capabilities in comparison to a number of state of the art methods.
Keyword(s): Computational complexity; Machine learning; Artificial intelligence; Algorithms
Publication Date:
2017
Type: Other
Peer-Reviewed: Unknown
Language(s): English
Contributor(s): McAuley, John; McKeever, Susan
Institution: Dublin City University
Publisher(s): CEUR-WS
File Format(s): application/pdf
Related Link(s): http://doras.dcu.ie/22079/,
http://ceur-ws.org/Vol-2086/AICS2017_paper_24.pdf
First Indexed: 2018-01-11 06:05:07 Last Updated: 2018-07-21 06:09:03