Comparison of Near Neighbour and Neural Network in Travel Forecasting

Olmedo, Elena

doi:10.1002/for.2370

Cited by 16 publications

(10 citation statements)

References 35 publications

(42 reference statements)

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“…Theoretically it strengthens the assumption that a neural network model performs better than linear models when predicting nonlinear variables [10], [13], [24]. From the application perspective, SDNM based on PSR provides an effect alternative to learn the chaotic propensities of tourism time series.…”

Section: Discussionmentioning

confidence: 56%

“…The most popular are the autoregressive integrated moving average models [3]- [5], the naive method [6], [7], and the exponential smoothing model [8]. However, the predictions obtained using these traditional models are usually imprecise, and it is difficult to utilize these models to approximate nonlinear and irregular tourism time series [9], [10].…”

Section: Introductionmentioning

confidence: 99%

“…A regime switching detection and forecasting model is proposed in [12]. However, the performance of these models is limited to the problems of proper model selection and data dependency [10], [13].…”

Section: Introductionmentioning

confidence: 99%

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Using a Single Dendritic Neuron to Forecast Tourist Arrivals to Japan

Chen

Sun

Gao

et al. 2017

IEICE Trans. Inf. & Syst.

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SUMMARYWith 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.

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Section: Discussionmentioning

confidence: 56%

Section: Introductionmentioning

confidence: 99%

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Using a Single Dendritic Neuron to Forecast Tourist Arrivals to Japan

Chen

Sun

Gao

et al. 2017

IEICE Trans. Inf. & Syst.

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“…The training period is collected from Jan.2011 to Dec.2014 while the testing period is collected from Jan.2015 to Mar.2015 as shown in Table 1. Month 2011 2012 2013 2014 1 12401 62586 35716 51794 2 110312 51592 136561 115373 3 31318 46754 53621 71695 4 53930 77309 66218 88102 5 45641 58647 54503 93649 6 36966 51058 48664 69080 7 48496 62686 63016 89325 8 44389 61646 68931 104956 9 35402 55845 65295 81669 10 63650 88176 96656 133420 11 31823 49599 52369 77835 12 21671 33237 56710 49695 According to algorithm 3, the experiment selected = (5,6,7,8,9,10,11,12,13,14,15,16,17,18,19,20,21,22,23,24,25,26,27,<...>…”

Section: Resultsmentioning

confidence: 99%

“…From 2008 to 2016, the article on the topic of tourism prediction, most of the journals used artificial intelligence methods [2], grey model [3], ARMA model [4], Markov model [5], regression model [6], neural network model [7], support vector machine model [8], and only a few papers used hybrid forecasting model. Time series forecasting model is mainly based on historical tourist arrivals and the current tourist arrivals.…”

Section: Introductionmentioning

confidence: 99%

A Hybrid Tourism Demand Forecasting Model Based on Fuzzy Times Series

Li¹,

Cao²,

Meng³

2017

dtcse

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Abstract. In previous studies, time series model has been the most common method and fuzzy time series model is an improvement on time series model. However, the accuracy in fuzzy time series model has been inevitably affected by interval length and the partitioning method for formulating effective interval can be very difficult. In addition to time series model, the grey model has also been widely studied, but it may have bad curve-fitting effects when data show great randomness. Therefore, both fuzzy time series model and grey model have problems in accuracy for forecasting tourism flow. Based on fuzzy time series model and grey model, this paper proposed a hybrid model which has been optimized by entropy and Markov model. In this hybrid model, the weight is calculated by entropy method which has been used to balance the performance of two single models. Markov model with its stable property is used for processing data sequence with large fluctuation. The result of the experiment clearly shows that this proposed hybrid model optimized the interval partition and therefore ensured the prediction accuracy.

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Forecasting air passenger travel: A case study of Norwegian aviation industry

Anupam,

Lawal

2023

Journal of Forecasting

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Accurate forecasting of airline passenger traffic is important for facilitating the effective management and planning of aviation resources. In this study, we explore the air passenger traffic in the Norwegian aviation industry by collecting the passenger flow data and the corresponding measurements of the weather conditions affecting the flow from the different airports in Norway. We then proposed nonlinear autoregressive with exogenous input (NARX) forecasting models to predict air passenger traffic in advance. The NARX models account for the nonlinearity and nonstationarity in the passenger flow and allow the accurate forecasting of air passenger traffic. We perform experiments to demonstrate the effectiveness of two variants of the NARX model and compare their performances against long short‐term memory (LSTM), a deep learning method. We show that the proposed NARX model achieves the best prediction accuracy compared to LSTM, which is considered as a state‐of‐the‐art approach for fitting sequential data.

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Comparison of Near Neighbour and Neural Network in Travel Forecasting

Cited by 16 publications

References 35 publications

Using a Single Dendritic Neuron to Forecast Tourist Arrivals to Japan

Using a Single Dendritic Neuron to Forecast Tourist Arrivals to Japan

A Hybrid Tourism Demand Forecasting Model Based on Fuzzy Times Series

Forecasting air passenger travel: A case study of Norwegian aviation industry

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