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Multi-resolution forecast aggregation for time series in agri datasets

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.

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Abstract

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.

Item Type:Conference or Workshop Item (Paper)
Event Type:Conference
Refereed:Yes
Uncontrolled Keywords: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;
Subjects:Computer Science > Computational complexity
Computer Science > Machine learning
Computer Science > Artificial intelligence
Computer Science > Algorithms
DCU Faculties and Centres:Research Initiatives and Centres > INSIGHT Centre for Data Analytics
DCU Faculties and Schools > Faculty of Engineering and Computing > School of Computing
Published in:Irish Conference on Artificial Intelligence and Cognitive Science (AICS 2017), Proceedings. .
Copyright Information:© 2017 The Authors
Use License:This item is licensed under a Creative Commons Attribution-NonCommercial-Share Alike 3.0 License. View License
Funders: Science Foundation Ireland grant number SFI/12/RC/2289
ID Code:22079
Deposited On:10 Jan 2018 16:41 by Thomas Murtagh. Last Modified 08 Feb 2018 14:39

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