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Time series model selection with a meta-learning approach; evidence from a pool of forecasting algorithms

Time series model selection with a meta-learning approach; evidence from a pool of forecasting algorithms
Time series model selection with a meta-learning approach; evidence from a pool of forecasting algorithms
One of the challenging questions in time series forecasting is how to find the best algorithm. In recent years, a recommender system scheme has been developed for time series analysis using a meta-learning approach. This system selects the best forecasting method with consideration of the time series characteristics. In this paper, we propose a novel approach to focusing on some of the unanswered questions resulting from the use of meta-learning in time series forecasting. Therefore, three main gaps in previous works are addressed including, analyzing various subsets of top forecasters as inputs for meta-learners; evaluating the effect of forecasting error measures; and assessing the role of the dimensionality of the feature space on the forecasting errors of meta-learners. All of these objectives are achieved with the help of a diverse state-of-the-art pool of forecasters and meta-learners. For this purpose, first, a pool of forecasting algorithms is implemented on the NN5 competition dataset and ranked based on the two error measures. Then, six machine-learning classifiers known as meta-learners, are trained on the extracted features of the time series in order to assign the most suitable forecasting method for the various subsets of the pool of forecasters. Furthermore, two-dimensionality reduction methods are implemented in order to investigate the role of feature space dimension on the performance of meta-learners. In general, it was found that meta-learners were able to defeat all of the individual benchmark forecasters; this performance was improved even after applying the feature selection method.
stat.ML, cs.LG
https://arxiv.org/abs/1908.08489
Barak, Sasan
f82186de-f5b7-4224-9621-a00e7501f2c3
Nasiri, Mahdi
30ae4b7f-8bcc-47fe-94d3-67e817a34940
Rostamzadeh, Mehrdad
0b97c582-52ed-4a70-9a3e-78db4565fcd0
Barak, Sasan
f82186de-f5b7-4224-9621-a00e7501f2c3
Nasiri, Mahdi
30ae4b7f-8bcc-47fe-94d3-67e817a34940
Rostamzadeh, Mehrdad
0b97c582-52ed-4a70-9a3e-78db4565fcd0

Barak, Sasan, Nasiri, Mahdi and Rostamzadeh, Mehrdad (2019) Time series model selection with a meta-learning approach; evidence from a pool of forecasting algorithms. arXiv. (https://arxiv.org/abs/1908.08489).

Record type: Article

Abstract

One of the challenging questions in time series forecasting is how to find the best algorithm. In recent years, a recommender system scheme has been developed for time series analysis using a meta-learning approach. This system selects the best forecasting method with consideration of the time series characteristics. In this paper, we propose a novel approach to focusing on some of the unanswered questions resulting from the use of meta-learning in time series forecasting. Therefore, three main gaps in previous works are addressed including, analyzing various subsets of top forecasters as inputs for meta-learners; evaluating the effect of forecasting error measures; and assessing the role of the dimensionality of the feature space on the forecasting errors of meta-learners. All of these objectives are achieved with the help of a diverse state-of-the-art pool of forecasters and meta-learners. For this purpose, first, a pool of forecasting algorithms is implemented on the NN5 competition dataset and ranked based on the two error measures. Then, six machine-learning classifiers known as meta-learners, are trained on the extracted features of the time series in order to assign the most suitable forecasting method for the various subsets of the pool of forecasters. Furthermore, two-dimensionality reduction methods are implemented in order to investigate the role of feature space dimension on the performance of meta-learners. In general, it was found that meta-learners were able to defeat all of the individual benchmark forecasters; this performance was improved even after applying the feature selection method.

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Published date: 22 August 2019
Keywords: stat.ML, cs.LG

Identifiers

Local EPrints ID: 434725
URI: https://eprints.soton.ac.uk/id/eprint/434725
DOI: https://arxiv.org/abs/1908.08489
PURE UUID: dfeafe82-751d-4b1e-8cdb-b4a6d0eaf392
ORCID for Sasan Barak: ORCID iD orcid.org/0000-0001-7715-9958

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Date deposited: 07 Oct 2019 16:30
Last modified: 08 Oct 2019 00:20

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