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Multistep-ahead prediction: a comparison of analytical and algorithmic approaches

Bahrpeyma, Fouad ORCID: 0000-0002-5128-4774, Roantree, Mark ORCID: 0000-0002-1329-2570 and McCarren, Andrew ORCID: 0000-0002-7297-0984 (2018) Multistep-ahead prediction: a comparison of analytical and algorithmic approaches. In: 20th International Conference on Big Data Analytics and Knowledge Discovery - DaWaK 2018, September 3 - 6, 2018, Regensburg, Germany. ISBN 978-3-319-98538-1

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Abstract

Most approaches to forecasting time series data employ one-step-ahead prediction approaches. However, recently there has been focus on multi-step-ahead prediction approaches. These approaches demonstrate enhanced prediction capabilities. However, multi-step-ahead prediction increases the complexity of the prediction process in comparison to one-step-ahead approaches. Typically, studies in the examination of multi-step ahead methods have addressed issues such as the increased complexity, inaccuracy, uncertainty, and error variance on the prediction horizon, and have been deployed in various domains such as finance, economics, agriculture and hydrology. When determining which algorithm to use in a time series analyses, the approach is to analyze the series for numerous characteristics and features, such as heteroscedasticity, auto-correlation, seasonality and stationarity. In this work, a comparative analysis of 20 different time series datasets is presented and a demonstration of the complexity in deciding which approach to use is given. The study investigates some of the main prediction approaches such as ARIMA (Autoregressive integrated moving average), NN (Neural Network), RNN (Recurrent neural network) and SVR (Support vector regression), which focus on the recursive prediction strategy and compare them to a new approach known as MRFA (Multi-Resolution Forecast Aggregation).

Item Type:Conference or Workshop Item (Paper)
Event Type:Conference
Refereed:Yes
Subjects:Computer Science > Machine learning
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: Big Data Analytics and Knowledge Discovery. Lecture Notes in Computer Science 11031. Springer. ISBN 978-3-319-98538-1
Publisher:Springer
Official URL:https://doi.org/10.1007/978-3-319-98539-8_26
Copyright Information:© 2018 Springer
Use License:This item is licensed under a Creative Commons Attribution-NonCommercial-Share Alike 3.0 License. View License
Funders:Science Foundation Ireland under grant number SFI/12/RC/2289
ID Code:22390
Deposited On:23 Jul 2018 11:53 by Fouad Bahrpeyma . Last Modified 10 Mar 2021 17:58

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