Using a Single Dendritic Neuron to Forecast Tourist Arrivals to Japan

Wei CHEN; Jian SUN; Shangce GAO; Jiu-Jun CHENG; Jiahai WANG; Yuki TODO

doi:10.1587/transinf.2016EDP7152

Using a Single Dendritic Neuron to Forecast Tourist Arrivals to Japan

Wei CHEN, Jian SUN, Shangce GAO, Jiu-Jun CHENG, Jiahai WANG, Yuki TODO

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With the fast growth of the international tourism industry, it has been a challenge to forecast the tourism demand in the international tourism market. Traditional forecasting methods usually suffer from the prediction accuracy problem due to the high volatility, irregular movements and non-stationarity of the tourist time series. In this study, a novel single dendritic neuron model (SDNM) is proposed to perform the tourism demand forecasting. First, we use a phase space reconstruction to analyze the characteristics of the tourism and reconstruct the time series into proper phase space points. Then, the maximum Lyapunov exponent is employed to identify the chaotic properties of time series which is used to determine the limit of prediction. Finally, we use SDNM to make a short-term prediction. Experimental results of the forecasting of the monthly foreign tourist arrivals to Japan indicate that the proposed SDNM is more efficient and accurate than other neural networks including the multi-layered perceptron, the neuro-fuzzy inference system, the Elman network, and the single multiplicative neuron model.

Publication: IEICE TRANSACTIONS on Information Vol.E100-D No.1 pp.190-202

Publication Date: 2017/01/01

Publicized: 2016/10/18

Online ISSN: 1745-1361

DOI: 10.1587/transinf.2016EDP7152

Type of Manuscript: PAPER

Category: Biocybernetics, Neurocomputing

Authors

Wei CHEN
  University of Toyama
Jian SUN
  University of Toyama,Taizhou University
Shangce GAO
  University of Toyama
Jiu-Jun CHENG
  Tongji University
Jiahai WANG
  Sun Yat-sen University
Yuki TODO
  Kanazawa University

Keyword

artificial neural networks, chaos, dendritic neuron model, phase space reconstruction, time series prediction, tourism demand

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Wei CHEN, Jian SUN, Shangce GAO, Jiu-Jun CHENG, Jiahai WANG, Yuki TODO, "Using a Single Dendritic Neuron to Forecast Tourist Arrivals to Japan" in IEICE TRANSACTIONS on Information, vol. E100-D, no. 1, pp. 190-202, January 2017, doi: 10.1587/transinf.2016EDP7152.
Abstract: With the fast growth of the international tourism industry, it has been a challenge to forecast the tourism demand in the international tourism market. Traditional forecasting methods usually suffer from the prediction accuracy problem due to the high volatility, irregular movements and non-stationarity of the tourist time series. In this study, a novel single dendritic neuron model (SDNM) is proposed to perform the tourism demand forecasting. First, we use a phase space reconstruction to analyze the characteristics of the tourism and reconstruct the time series into proper phase space points. Then, the maximum Lyapunov exponent is employed to identify the chaotic properties of time series which is used to determine the limit of prediction. Finally, we use SDNM to make a short-term prediction. Experimental results of the forecasting of the monthly foreign tourist arrivals to Japan indicate that the proposed SDNM is more efficient and accurate than other neural networks including the multi-layered perceptron, the neuro-fuzzy inference system, the Elman network, and the single multiplicative neuron model.
URL: https://globals.ieice.org/en_transactions/information/10.1587/transinf.2016EDP7152/_p

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@ARTICLE{e100-d_1_190,
author={Wei CHEN, Jian SUN, Shangce GAO, Jiu-Jun CHENG, Jiahai WANG, Yuki TODO, },
journal={IEICE TRANSACTIONS on Information},
title={Using a Single Dendritic Neuron to Forecast Tourist Arrivals to Japan},
year={2017},
volume={E100-D},
number={1},
pages={190-202},
abstract={With the fast growth of the international tourism industry, it has been a challenge to forecast the tourism demand in the international tourism market. Traditional forecasting methods usually suffer from the prediction accuracy problem due to the high volatility, irregular movements and non-stationarity of the tourist time series. In this study, a novel single dendritic neuron model (SDNM) is proposed to perform the tourism demand forecasting. First, we use a phase space reconstruction to analyze the characteristics of the tourism and reconstruct the time series into proper phase space points. Then, the maximum Lyapunov exponent is employed to identify the chaotic properties of time series which is used to determine the limit of prediction. Finally, we use SDNM to make a short-term prediction. Experimental results of the forecasting of the monthly foreign tourist arrivals to Japan indicate that the proposed SDNM is more efficient and accurate than other neural networks including the multi-layered perceptron, the neuro-fuzzy inference system, the Elman network, and the single multiplicative neuron model.},
keywords={},
doi={10.1587/transinf.2016EDP7152},
ISSN={1745-1361},
month={January},}

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TY - JOUR
TI - Using a Single Dendritic Neuron to Forecast Tourist Arrivals to Japan
T2 - IEICE TRANSACTIONS on Information
SP - 190
EP - 202
AU - Wei CHEN
AU - Jian SUN
AU - Shangce GAO
AU - Jiu-Jun CHENG
AU - Jiahai WANG
AU - Yuki TODO
PY - 2017
DO - 10.1587/transinf.2016EDP7152
JO - IEICE TRANSACTIONS on Information
SN - 1745-1361
VL - E100-D
IS - 1
JA - IEICE TRANSACTIONS on Information
Y1 - January 2017
AB - With the fast growth of the international tourism industry, it has been a challenge to forecast the tourism demand in the international tourism market. Traditional forecasting methods usually suffer from the prediction accuracy problem due to the high volatility, irregular movements and non-stationarity of the tourist time series. In this study, a novel single dendritic neuron model (SDNM) is proposed to perform the tourism demand forecasting. First, we use a phase space reconstruction to analyze the characteristics of the tourism and reconstruct the time series into proper phase space points. Then, the maximum Lyapunov exponent is employed to identify the chaotic properties of time series which is used to determine the limit of prediction. Finally, we use SDNM to make a short-term prediction. Experimental results of the forecasting of the monthly foreign tourist arrivals to Japan indicate that the proposed SDNM is more efficient and accurate than other neural networks including the multi-layered perceptron, the neuro-fuzzy inference system, the Elman network, and the single multiplicative neuron model.
ER -