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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; Time series prediction; Multistep ahead prediction; Forecasting; The small sample size problem; Recurrent Neural Networks; Forecast Aggregation; Multi-resolution Analysis; Prediction in the long run; Resolution of Impact (ROI); Univariate time series; Time series analysis
Publication Date:
2017
Type: Conference item
Peer-Reviewed: Yes
Language(s): English
Institution: Dublin City University
Citation(s): Bahrpeyma, Fouad and Roantree, Mark and McCarren, Andrew (2017) Multi-resolution forecast aggregation for time series in agri datasets. In: Irish Conference on Artificial Intelligence and Cognitive Science (AICS 2017), 7-8 Dec 2017, Dublin, Ireland.
File Format(s): application/pdf
First Indexed: 2018-01-11 06:05:07 Last Updated: 2018-01-12 06:05:07