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Effective long short-term memory with fruit fly optimization algorithm for time series forecasting

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Abstract

A number of recent studies have adopted long short-term memory (LSTM) in extensive applications, such as handwriting recognition and time series prediction, with considerable success. However, the parameters of LSTM have greatly influenced its accuracy and performance. In this study, LSTM with fruit fly optimization algorithm (FOA), called FOA-LSTM, is designed to solve time series problems. As a novel intelligent algorithm, FOA is applied to decide on the optimal hyper-parameter of LSTM. Experiments under the NN3 time series, three comparative experiments and the monthly energy consumption of the USA are conducted to verify the effectiveness of the FOA-LSTM model. The results indicate that the symmetric mean absolute percentage error (SMAPE) is reduced by up to 11.44% in the last 11 monthly series in the NN3 dataset. Four comparative experiments and the real-life series verify further that the FOA-LSTM model obtains a better result compared with other forecasting models.

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(Source: Peng et al. 2018)

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(Source: Lv et al. 2017)

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Acknowledgements

The authors are very grateful for the constructive comments of editors and referees. This research is partially supported by National Natural Science Foundation of China (No. 71771095) and Humanities and Social Sciences Foundation of Chinese Ministry of Education, China (No. 18YJA630005).

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Authors and Affiliations

  1. School of Management, Huazhong University of Science and Technology, Wuhan, 430074, China

    Lu Peng, Sheng-Xiang Lv & Lin Wang

  2. International Business School, Shaanxi Normal University, Xi’an, 710000, China

    Qing Zhu

Authors
  1. Lu Peng
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  2. Qing Zhu
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  3. Sheng-Xiang Lv
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  4. Lin Wang
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Correspondence to Lin Wang.

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Communicated by V. Loia.

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Peng, L., Zhu, Q., Lv, SX. et al. Effective long short-term memory with fruit fly optimization algorithm for time series forecasting. Soft Comput 24, 15059–15079 (2020). https://doi.org/10.1007/s00500-020-04855-2

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