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Purpose: Accurate foreign tourist arrivals forecasting can help public and private sectors to formulate scientific tourism planning and improve the allocation efficiency of tourism resources. This paper aims to address the problem of low prediction accuracy of Chinese inbound tourism demand caused by the lack of valid historical data. Design/methodology/approach: A novel hybrid Chinese inbound tourism demand forecasting model combining fractional non-homogenous discrete grey model and firefly algorithm is constructed. In the proposed model, all adjustable parameters of the fractional non-homogenous discrete grey model are optimized simultaneously by the firefly algorithm. Findings: The data sets of annual foreign tourist arrivals to China are used to verify the validity of the proposed model. Experimental results show that the proposed method is effective and can be used as a useful predictor for the prediction of Chinese inbound tourism demand. Originality/value: The method proposed in this paper is effective and can be used as a feasible approach for forecasting the development trend of Chinese inbound tourism. [ABSTRACT FROM AUTHOR]

Purpose: The purpose of this paper is to develop a hybrid algorithm, which is a blend of auto-regressive integral moving average (ARIMA) and multilayer perceptron (MLP) for addressing the non-linearity of the load time series. Design/methodology/approach: Short-term load forecasting is a complex process as the nature of the load-time series data is highly nonlinear. So, only ARIMA-based load forecasting will not provide accurate results. Hence, ARIMA is combined with MLP, a deep learning approach that models the resultant data from ARIMA and processes them further for Modelling the non-linearity. Findings: The proposed hybrid approach detects the residuals of the ARIMA, a linear statistical technique and models these residuals with MLP neural network. As the non-linearity of the load time series is approximated in this error modeling process, the proposed approach produces accurate forecasting results of the hourly loads. Originality/value: The effectiveness of the proposed approach is tested in the laboratory with the real load data of a metropolitan city from South India. The performance of the proposed hybrid approach is compared with the conventional methods based on the metrics such as mean absolute percentage error and root mean square error. The comparative results show that the proposed prediction strategy outperforms the other hybrid methods in terms of accuracy. [ABSTRACT FROM AUTHOR]

Purpose – The purpose of this paper is to construct a novel GM(0, N) model based on grey number sequences and to solve the problem of cost forecasting of commercial aircraft which puzzled managers, especially the factors of cost with uncertain information. Design/methodology/approach – Based on the definition of traditional GM(0, N) model, the paper considers the limited information and knowledge, and the algorithm of grey numbers with greyness and kernel was designed. A novel GM(0, N) model based on grey number sequences, named the IN-GM(0, N) model, is proposed according to the definition of the grey numbers algorithm. The steps of the proposed model are then given. Finally, a case of domestic commercial aircrafts is developed as an example, based on information gathering and model calculating. Findings – The results of this research indicate that the IN-GM(0,N) model is effective in cost calculating, providing reliable technical support for cost estimation of large-scale complex equipment including commercial aircraft. Practical implications – Cost forecasting of commercial aircraft can be solved effectively and the model can also be utilized to predicate other products. Originality/value – The paper succeeds in constructing a novel grey forecasting model. This work contributes significantly to improving grey forecasting theory and to undoubtedly propose more novel grey forecasting models. [ABSTRACT FROM AUTHOR]