Research Article


DOI :10.5152/iujsb.2017.001   IUP :10.5152/iujsb.2017.001    Full Text (PDF)

Comparing and Combining MLP and NEAT for Time Series Forecasting

Serkan ArasAnh NguyenAllan WhiteShan He

Neural networks are one of the widely-used time series forecasting methods in time series applications. Among different neural network architectures and learning algorithms, the most popular choice is the feedforward Multilayer Perceptron (MLP). However, it suffers from some drawbacks such as getting trapped in local minima, human intervention during the stage of training, and limitations in architecture design. The aims of this study were twofold. The first was to employ NeuroEvolution of Augmenting Topologies (NEAT), which has many successful applications in numerous fields. In this paper, we applied it to time series forecasting for the first time and compared its performance with that of the MLP. The second aim was to analyse the performance resulting from the pairwise combination of these methods. In general, the results suggested that the forecasts from the NEAT algorithm were more accurate than those of the MLP. The results also showed that pairwise combined forecasts in general were better than single forecasts. The best forecasts of all were obtained by pairwise combination of MLP and NEAT.


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APA

Aras, S., Nguyen, A., White, A., & He, S. (2017). Comparing and Combining MLP and NEAT for Time Series Forecasting. Istanbul Business Research, 46(2), 147-160. https://doi.org/10.5152/iujsb.2017.001


AMA

Aras S, Nguyen A, White A, He S. Comparing and Combining MLP and NEAT for Time Series Forecasting. Istanbul Business Research. 2017;46(2):147-160. https://doi.org/10.5152/iujsb.2017.001


ABNT

Aras, S.; Nguyen, A.; White, A.; He, S. Comparing and Combining MLP and NEAT for Time Series Forecasting. Istanbul Business Research, [Publisher Location], v. 46, n. 2, p. 147-160, 2017.


Chicago: Author-Date Style

Aras, Serkan, and Anh Nguyen and Allan White and Shan He. 2017. “Comparing and Combining MLP and NEAT for Time Series Forecasting.” Istanbul Business Research 46, no. 2: 147-160. https://doi.org/10.5152/iujsb.2017.001


Chicago: Humanities Style

Aras, Serkan, and Anh Nguyen and Allan White and Shan He. Comparing and Combining MLP and NEAT for Time Series Forecasting.” Istanbul Business Research 46, no. 2 (Oct. 2022): 147-160. https://doi.org/10.5152/iujsb.2017.001


Harvard: Australian Style

Aras, S & Nguyen, A & White, A & He, S 2017, 'Comparing and Combining MLP and NEAT for Time Series Forecasting', Istanbul Business Research, vol. 46, no. 2, pp. 147-160, viewed 2 Oct. 2022, https://doi.org/10.5152/iujsb.2017.001


Harvard: Author-Date Style

Aras, S. and Nguyen, A. and White, A. and He, S. (2017) ‘Comparing and Combining MLP and NEAT for Time Series Forecasting’, Istanbul Business Research, 46(2), pp. 147-160. https://doi.org/10.5152/iujsb.2017.001 (2 Oct. 2022).


MLA

Aras, Serkan, and Anh Nguyen and Allan White and Shan He. Comparing and Combining MLP and NEAT for Time Series Forecasting.” Istanbul Business Research, vol. 46, no. 2, 2017, pp. 147-160. [Database Container], https://doi.org/10.5152/iujsb.2017.001


Vancouver

Aras S, Nguyen A, White A, He S. Comparing and Combining MLP and NEAT for Time Series Forecasting. Istanbul Business Research [Internet]. 2 Oct. 2022 [cited 2 Oct. 2022];46(2):147-160. Available from: https://doi.org/10.5152/iujsb.2017.001 doi: 10.5152/iujsb.2017.001


ISNAD

Aras, Serkan - Nguyen, Anh - White, Allan - He, Shan. Comparing and Combining MLP and NEAT for Time Series Forecasting”. Istanbul Business Research 46/2 (Oct. 2022): 147-160. https://doi.org/10.5152/iujsb.2017.001



TIMELINE


Submitted17.08.2016
Last Revision17.09.2017
Accepted17.09.2017

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