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Article
Publication date: 6 February 2017

Yi-Chung Hu

Energy demand is an important economic index, and demand forecasting has a significant role when devising energy development plans for cities or countries. GM(1,1) model…

Abstract

Purpose

Energy demand is an important economic index, and demand forecasting has a significant role when devising energy development plans for cities or countries. GM(1,1) model has become popular because it needs only a few data points to construct a time-series model without statistical assumptions. Several methods have been developed to improve prediction accuracy of the original GM(1,1) model by only estimating the sign of each residual. This study aims to address that this is too tight a restriction for the modification range.

Design/methodology/approach

Based on the predicted residual, this study uses the functional-link net (FLN) with genetic-algorithm-based learning to estimate the modification range for its corresponding predicted value obtained from the original GM(1,1) model.

Findings

The forecasting ability of the proposed grey prediction model is verified using real energy demand cases from China. Experimental results show that the proposed prediction model performs well compared to other grey residual modification models with sign estimation.

Originality/value

The proposed FLNGM(1,1) model can improve prediction accuracy of the original GM(1,1) model using residual modification. The distinctive feature of the proposed model is to use an FLN to estimate sign and modification range simultaneously for the predicted value based on its corresponding predicted residual obtained from the residual GM(1,1) model.

Details

Kybernetes, vol. 46 no. 2
Type: Research Article
ISSN: 0368-492X

Keywords

Article
Publication date: 28 June 2022

Yi-Chung Hu and Geng Wu

Given that the use of Google Trends data is helpful to improve forecasting performance, this study aims to investigate whether the precision of forecast combination can…

Abstract

Purpose

Given that the use of Google Trends data is helpful to improve forecasting performance, this study aims to investigate whether the precision of forecast combination can benefit from the use of Google Trends Web search index along with the encompassing set.

Design/methodology/approach

Grey prediction models generate single-model forecasts, while Google Trends index serves as an explanatory variable for multivariate models. Then, three combination sets, including sets of univariate models (CUGM), all constituents (CAGM) and constituents that survive the forecast encompassing tests (CSET), are generated. Finally, commonly used combination methods combine the individual forecasts for each combination set.

Findings

The tourism volumes of four frequently searched-for cities in Taiwan are used to evaluate the accuracy of three combination sets. The encompassing tests show that multivariate grey models play a role to be reckoned with in forecast combinations. Furthermore, the empirical results indicate the usefulness of Google Trends index and encompassing tests for linear combination methods because linear combination methods coupled with CSET outperformed that coupled with CAGM and CUGM.

Practical implications

With Google Trends Web search index, the tourism sector may benefit from the use of linear combinations of constituents that survive encompassing tests to formulate business strategies for tourist destinations. A good forecasting practice by estimating ex ante forecasts post-COVID-19 can be further provided by scenario forecasting.

Originality/value

To improve the accuracy of combination forecasting, this research verifies the correlation between Google Trends index and combined forecasts in tourism along with encompassing tests.

Google 搜尋趨勢指標與涵蓋性檢定對於旅遊需求組合預測的影響

目的

過去的研究顯示 Google 搜尋趨勢資料有助於改善旅遊需求預測的準確度,本研究就此進一步探討 Google 搜尋趨勢網頁搜尋指標與涵蓋性檢定的使用對於組合預測準確度所造成的影響。

設計/方法論/方法

本研究以 Google 搜尋趨勢指標做為多變量灰色預測模式的解釋變數,並以單變量與多變量灰色模式產生各別預測值。在分別產生由所有的單變量模式 (CUGM)所有的模式 (CAGM), 以及經過涵蓋性檢定所留存下來之模式 (CSET) 所組成之集合後,就各別的組合集以常用的組合方法產生預測值。

發現

以台灣的四個熱搜旅遊城市的旅遊人數進行三個組合集的預測準確度分析。涵蓋性檢定顯示多變量灰色模式在組合預測中扮演重要的角色,而結果亦呈現線性組合方法在 CSET優於在 CUGMCAGM 的準確度,突顯搜尋趨勢指標與涵蓋性檢定對於線性組合方法的有用性。

實踐意涵

藉由 Google 搜尋趨勢網頁搜尋指標與涵蓋性檢定,旅遊部門應可透過線性組合方法的預測規劃旅遊目的地的經營策略。新冠疫情下於各季的事前預測亦可結合情境預測具體呈現。

原創性/價值

為提升組合預測在旅遊需求的預測準確度,本研究結合涵蓋性檢定以分析 Google 搜尋趨勢指標與組合預測準確度之間的關聯性。

關鍵字

旅遊需求,涵蓋性檢定,Google 搜尋趨勢,灰色預測,組合預測

文章类型

研究型论文

El impacto de Google Trends en la previsión de viajes combinados y su evidencia relacionada

Propósito

Dado que el uso de los datos de Google Trends es útil para mejorar la precisión de las predicciones, este estudio examina si el uso del índice de búsqueda web de Google Trends combinado con la agregación de relevancia puede mejorar la precisión del predictor.

Diseño/metodología/enfoque

El modelo predictivo gris genera predicciones bajo un único modelo, mientras que el modelomultivariado utiliza el indicador Google Trends como variable explicativa. Se generaron tresensamblajes generales, incluido el Modelo armónico único (CUGM), los ensamblajes de todos loscomponentes (CAGM) y la prueba de presencia de componentes con predicción (CSET). Laspredicciones individuales encada grupo luego se combinan utilizando métodos de correlación deuso común.

Recomendaciones

Utilizando el número de turistas en las cuatro ciudades más investigadas de Taiwán, los tresgrupos combinados se clasificaron según su precisión. Las pruebas incluidas muestran que losmodelos multivariados en escala de grises son importantes para la predicción. Además, losresultados de las pruebas muestran que el índice de Google Trends y las pruebas que incluyenmétodos de suma lineal son útiles porque los métodos combinados con CSET funcionan majorque los métodos combinados con CSET. CAGM y VCUG.

Implicaciones practices

La industria de viajes puede usar el índice de búsqueda web de Google Trends para desarrollarestrategias comerciales para atracciones basadas en un conjunto lineal de componentes.

Autenticidad/valor

Con el objetivo de mejorar la precisión de los pronósticos agregados, este estudio investiga larelación entre el índice de tendencias de Google y las expectativas generales de viaje junto con laevidencia global.

Palabras clave

Demanda de viajes, Experiencia global, Tendencias de Google, Predicción gris

Tipo de papel

Trabajo de investigación

Article
Publication date: 1 March 2022

Yi-Chung Hu

This study aims to address three important issues of combination forecasting in the tourism context: reducing the restrictions arising from requirements related to the…

Abstract

Purpose

This study aims to address three important issues of combination forecasting in the tourism context: reducing the restrictions arising from requirements related to the statistical properties of the available data, assessing the weights of single models and considering nonlinear relationships among combinations of single-model forecasts.

Design Methodology Approach

A three-stage multiple-attribute decision-making (MADM)-based methodological framework was proposed. Single-model forecasts were generated by grey prediction models for the first stage. Vlsekriterijumska Optimizacija I Kompromisno Resenje was adopted to develop a weighting scheme in the second stage, and the Choquet integral was used to combine forecasts nonlinearly in the third stage.

Findings

The empirical results for inbound tourism in Taiwan showed that the proposed method can significantly improve accuracy to a greater extent than other combination methods. Along with scenario forecasting, a good forecasting practice can be further provided by estimating ex-ante forecasts post-COVID-19.

Practical Implications

The private and public sectors in economies with high tourism dependency can benefit from the proposed method by using the forecasts to help them formulate tourism strategies.

Originality Value

This study contributed to presenting a MADM-based framework that advances the development of a more accurate combination method for tourism forecasting.

目的

針對旅遊需求預測, 本研究就降低對於資料統計性質的要求、模式的重要度評估, 以及各別預設值間存在的非線性關係等三項重要議題建立組合預測的研究框架。

設計/方法論/方法

研究方法以多屬性決策分析為基礎, 在實作上以灰色預測模式產生各別預測值、以 VIKOR 為模式發展加權方案, 再使用模糊積分以非線性方式組合預測值。

發現

以台灣的入境旅遊需求進行分析, 並與其他組合方法相較, 發現所提出方法的預測準確度顯著較佳。與情境預測結合下, 研究結果亦可呈現新冠疫情下於各季的事前預測。

實踐意涵

對旅遊具有高度依賴的經濟體, 所提出方法所產生的預測值有助於其公部門與私部門規劃旅遊策略。

原創性/價值

組合預測在旅遊需求的預測上有其研究價值。本研究在旅遊預測議題提出以多屬性決策分析為基礎之框架, 在推進具高準確率組合方法的發展上作出貢獻。

Propósito

La combinación de pronósticos en este estudio abordó tres cuestiones importantes para la situación del turismo: Reducir las restricciones que surgen con respecto a las estadísticas de datos disponibles, evaluar los pesos con un solo pronóstico, y considerar las relaciones no lineales entre las combinaciones con un único modelo de pronóstico.

Diseño/metodología/enfoque

Se propuso un marco metodológico de tres etapas basado en MADM. Un solo pronóstico fue generado mediante modelos de predicción grises para la primera etapa. Se aplicó VIKOR para desarrollar un esquema de ponderación en la segunda etapa, y la integral de Choquet se usó para combinar los pronósticos de manera no lineal en la tercera etapa.

Recomendaciones

Los resultados empíricos de la demanda turística entrante en Taiwán mostraron que el método propuesto puede mejorar efectivamente la precisión en mayor medida que otros métodos combinados. Una buena práctica del pronóstico puede proporcionar aún más, mediante las previsiones y la estimación exante de pronósticos posteriores al COVID-19.

Implicaciones practices

Los sectores públicos y privados de las economías con alta dependencia del turismo pueden beneficiarse del método propuesto al usar los pronósticos para ayudarlos a formular estrategias turísticas.

Autenticidad/valor

Este estudio contribuye a presentar un marco basado en MADM que avanza en el desarrollo de un método de combinación más preciso para la previsión del turismo.

Article
Publication date: 21 June 2019

Hang Jiang, Yi-Chung Hu, Jan-Yan Lin and Peng Jiang

With the development of economy, China’s OFDI constantly increase in recent year. Meanwhile, OFDI has spillover effect on economic development and technological…

Abstract

Purpose

With the development of economy, China’s OFDI constantly increase in recent year. Meanwhile, OFDI has spillover effect on economic development and technological development of home country. Thus, accurate OFDI prediction is a prerequisite for the effective development of international investment strategies. The purpose of this paper is to predict China’s OFDI accurately using a novel multivariable grey prediction model with Fourier series.

Design/methodology/approach

This paper applied a multivariable grey prediction model, GM(1,N), to forecast China’s OFDI. In order to improve the prediction accuracy and without changing local characteristics of grey model prediction, this paper proposed a novel grey prediction model to improve the performance of the traditional GM(1,N) model by combining with residual modification model using GM(1,1) model and Fourier series.

Findings

The coefficients indicate that the export and GDP have positive influence on China’s OFDI, and, according to the prediction result, China’s OFDI shows a growing trend in next five years.

Originality/value

This paper proposed an effective multivariable grey prediction model that combined the traditional GM(1,N) model with a residual modification model in order to predict China’s OFDI. Accurate forecasting of OFDI provides reference for the Chinese Government to implement international investment strategies.

Details

International Journal of Intelligent Computing and Cybernetics, vol. 12 no. 3
Type: Research Article
ISSN: 1756-378X

Keywords

Article
Publication date: 2 December 2022

Yi-Chung Hu

Forecasting tourism demand accurately can help private and public sector formulate strategic planning. Combining forecasting is feasible to improving the forecasting…

Abstract

Purpose

Forecasting tourism demand accurately can help private and public sector formulate strategic planning. Combining forecasting is feasible to improving the forecasting accuracy. This paper aims to apply multiple attribute decision-making (MADM) methods to develop new combination forecasting methods.

Design/methodology/approach

Grey relational analysis (GRA) is applied to assess weights for individual constituents, and the Choquet fuzzy integral is employed to nonlinearly synthesize individual forecasts from single grey models, which are not required to follow any statistical property, into a composite forecast.

Findings

The empirical results indicate that the proposed method shows the superiority in mean accuracy over the other combination methods considered.

Practical implications

For tourism practitioners who have no experience of using grey prediction, the proposed methods can help them avoid the risk of forecasting failure arising from wrong selection of one single grey model. The experimental results demonstrated the high applicability of the proposed nonadditive combination method for tourism demand forecasting.

Originality/value

By treating both weight assessment and forecast combination as MADM problems in the tourism context, this research investigates the incorporation of MADM methods into combination forecasting by developing weighting schemes with GRA and nonadditive forecast combination with the fuzzy integral.

Details

Grey Systems: Theory and Application, vol. ahead-of-print no. ahead-of-print
Type: Research Article
ISSN: 2043-9377

Keywords

Article
Publication date: 11 July 2022

Peng Jiang and Yi-Chung Hu

In contrast to point forecasts, interval forecasts provide information on future variability. This research thus aimed to develop interval prediction models by addressing…

Abstract

Purpose

In contrast to point forecasts, interval forecasts provide information on future variability. This research thus aimed to develop interval prediction models by addressing two significant issues: (1) a simple average with an additive property is commonly used to derive combined forecasts, but this unreasonably ignores the interaction among sequences used as sources of information, and (2) the time series often does not conform to any statistical assumptions.

Design/methodology/approach

To develop an interval prediction model, the fuzzy integral was applied to nonlinearly combine forecasts generated by a set of grey prediction models, and a sequence including the combined forecasts was then used to construct a neural network. All required parameters relevant to the construction of an interval model were optimally determined by the genetic algorithm.

Findings

The empirical results for tourism demand showed that the proposed non-additive interval model outperformed the other interval prediction models considered.

Practical implications

The private and public sectors in economies with high tourism dependency can benefit from the proposed model by using the forecasts to help them formulate tourism strategies.

Originality/value

In light of the usefulness of combined point forecasts and interval model forecasting, this research contributed to the development of non-additive interval prediction models on the basis of combined forecasts generated by grey prediction models.

Details

Grey Systems: Theory and Application, vol. 13 no. 1
Type: Research Article
ISSN: 2043-9377

Keywords

Article
Publication date: 8 July 2020

Peng Jiang, Wenbao Wang, Yi-Chung Hu, Yu-Jing Chiu and Shu-Ju Tsao

It is challenging to derive an appropriate tolerance relation for tolerance rough set-based classifiers (TRSCs). The traditional tolerance rough set employs a simple…

Abstract

Purpose

It is challenging to derive an appropriate tolerance relation for tolerance rough set-based classifiers (TRSCs). The traditional tolerance rough set employs a simple distance function to determine the tolerance relation. However, such a simple function does not take into account criterion weights and the interaction among criteria. Further, the traditional tolerance relation ignores interdependencies concerning direct and indirect influences among patterns. This study aimed to incorporate interaction and interdependencies into the tolerance relation to develop non-additive grey TRSCs (NG-TRSCs).

Design/methodology/approach

For pattern classification, this study applied non-additive grey relational analysis (GRA) and the decision-making trial and evaluation laboratory (DEMATEL) technique to solve problems arising from interaction and interdependencies, respectively.

Findings

The classification accuracy rates derived from the proposed NG-TRSC were compared to those of other TRSCs with distinctive features. The results showed that the proposed classifier was superior to the other TRSCs considered.

Practical implications

In addition to pattern classification, the proposed non-additive grey DEMATEL can further benefit the applications for managerial decision-making because it simplifies the operations for decision-makers and enhances the applicability of DEMATEL.

Originality/value

This paper contributes to the field by proposing the non-additive grey tolerance rough set (NG-TRS) for pattern classification. The proposed NG-TRSC can be constructed by integrating the non-additive GRA with DEMATEL by using a genetic algorithm to determine the relevant parameters.

Details

Grey Systems: Theory and Application, vol. 11 no. 1
Type: Research Article
ISSN: 2043-9377

Keywords

Article
Publication date: 18 June 2020

Yi-Chung Hu, Peng Jiang, Hang Jiang and Jung-Fa Tsai

In the face of complex and challenging economic and business environments, developing and implementing approaches to predict bankruptcy has become important for firms…

Abstract

Purpose

In the face of complex and challenging economic and business environments, developing and implementing approaches to predict bankruptcy has become important for firms. Bankruptcy prediction can be regarded as a grey system problem because while factors such as the liquidity, solvency and profitability of a firm influence whether it goes bankrupt, the precise manner in which these factors influence the discrimination between failed and non-failed firms is uncertain. In view of the applicability of multivariate grey prediction models (MGPMs), this paper aimed to develop a grey bankruptcy prediction model (GBPM) based on the GM (1, N) (BP-GM (1, N)).

Design/methodology/approach

As the traditional GM (1, N) is designed for time series forecasting, it is better to find an appropriate permutation of firms in the financial data as if the resulting sequences are time series. To solve this challenging problem, this paper proposes GBPMs by integrating genetic algorithms (GAs) into the GM (1, N).

Findings

Experimental results obtained for the financial data of Taiwanese firms in the information technology industries demonstrated that the proposed BP-GM (1, N) performs well.

Practical implications

Among artificial intelligence (AI)-based techniques, GBPMs are capable of explaining which of the financial ratios has a stronger impact on bankruptcy prediction by driving coefficients.

Originality/value

Applying MGPMs to a problem without relation to time series is challenging. This paper focused on bankruptcy prediction, a crucial issue in financial decision-making for businesses, and proposed several GBPMs.

Details

Grey Systems: Theory and Application, vol. 11 no. 1
Type: Research Article
ISSN: 2043-9377

Keywords

Article
Publication date: 30 December 2019

Quynh-Trang Nguyen, Ming-Yen Lee and Yi-Chung Hu

This study aims to concentrate on a specific perspective that has mostly been ignored: employees in social enterprises (SEs). It proposes that employees in SEs should be…

Abstract

Purpose

This study aims to concentrate on a specific perspective that has mostly been ignored: employees in social enterprises (SEs). It proposes that employees in SEs should be treated with equal importance to outside beneficiaries within the SEs’ value-creating mission.

Design/methodology/approach

A multiple case study approach is adopted, and semi-structured interviews are the primary means of data collection.

Findings

The results show that while economic values are fundamental for the employment relationship, social values play the leading role in employees’ motivation; thus, they can significantly affect the organization’s operation and development.

Research limitations/implications

This work contributes to Maslow’s need theory and psychological contract theory regarding their application to SEs. Practical lessons and suggestions are also provided for SEs’ development.

Originality/value

By emphasizing the value-creating mission of SEs with the new perspective of including employees in it, this work provides empirical evidence and practical lessons for SEs, especially Asian SEs, in terms of management and strategy.

Details

Social Enterprise Journal, vol. 16 no. 1
Type: Research Article
ISSN: 1750-8614

Keywords

Article
Publication date: 1 February 2016

Yi-Chung Hu

– The purpose of this paper is to propose that the grey tolerance rough set (GTRS) and construct the GTRS-based classifiers.

Abstract

Purpose

The purpose of this paper is to propose that the grey tolerance rough set (GTRS) and construct the GTRS-based classifiers.

Design/methodology/approach

The authors use grey relational analysis to implement a relationship-based similarity measure for tolerance rough sets.

Findings

The proposed classification method has been tested on several real-world data sets. Its classification performance is comparable to that of other rough-set-based methods.

Originality/value

The authors design a variant of a similarity measure which can be used to estimate the relationship between any two patterns, such that the closer the relationship, the greater the similarity will be.

Details

Kybernetes, vol. 45 no. 2
Type: Research Article
ISSN: 0368-492X

Keywords

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