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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 has…

223

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

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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 benefit…

458

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

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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 development of…

175

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

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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 statistical…

368

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.

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Article
Publication date: 21 January 2025

Yi-Chung Hu, Geng Wu and Jung-Fa Tsai

Linear addition is commonly used to generate ensemble forecasts for decomposition ensemble models but traditionally treats individual modes with equal weights for simplicity…

7

Abstract

Purpose

Linear addition is commonly used to generate ensemble forecasts for decomposition ensemble models but traditionally treats individual modes with equal weights for simplicity. Using Taiwan air passenger flow as an empirical case, this study examines whether incorporating weighting for individual single-mode forecasts assessed by grey relational analysis into linear addition can improve the accuracy of the decomposition ensemble models used to forecast air passenger demand.

Design/methodology/approach

Data series are decomposed into several single modes by empirical mode decomposition, and then different artificial intelligence methods are applied to individually forecast these decomposed modes. By incorporating the correlation between each forecasted mode series and the original time series into linear addition for ensemble learning, a genetic algorithm is applied to optimally synthesize individual single-mode forecasts to obtain the ensemble forecasts.

Findings

The empirical results in terms of level and directional forecasting accuracy showed that the proposed decomposition ensemble models with linear addition using grey relational analysis improved the forecasting accuracy of air passenger demand for different forecasting horizons.

Practical implications

Accurately forecasting air passenger demand is beneficial for both policymakers and practitioners in the aviation industry when making operational plans.

Originality/value

In light of the significance of improving the accuracy of decomposition ensemble models for forecasting air passenger demand, this research contributes to the development of a weighting scheme using grey relational analysis to generate ensemble forecasts.

Details

Grey Systems: Theory and Application, vol. 15 no. 1
Type: Research Article
ISSN: 2043-9377

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Article
Publication date: 31 July 2024

Lina Syazwana Kamaruzzaman, Yingxin Goh and Yi Chung Goh

This study aims to investigate the effect of incorporating cobalt (Co) into Sn-58Bi alloy on its phase composition, tensile properties, hardness and thermal aging performances…

33

Abstract

Purpose

This study aims to investigate the effect of incorporating cobalt (Co) into Sn-58Bi alloy on its phase composition, tensile properties, hardness and thermal aging performances. The fracture morphologies of tensile-tested solders are also investigated to correlate the microstructural changes with tensile properties of the solder alloys. Then, the thermal aging performances of the solder alloys are investigated in terms of their intermetallic compound (IMC) layer morphology and thickness.

Design/methodology/approach

The Sn-58Bi and Sn-58Bi-xCo, where x = 1.0, 1.5 and 2.0 Wt.%, were prepared using the flux doping technique. X-ray diffraction (XRD) is used to study the phase composition of the solder alloys, whereas scanning electron microscopy (SEM) and energy-dispersive X-ray spectroscopy (EDX) are used to investigate the microstructure, fractography and compositions of the solders. Tensile properties such as ultimate tensile strength (UTS), Young’s modulus and elongation are tested using the tensile test, whereas the microhardness value is gained from the micro-Vickers hardness test. The morphology and thickness of the IMC layer at the solder’s joints are investigated by varying the thermally aging duration up to 56 days at 80°C.

Findings

XRD analysis shows the presence of Co3Sn2 phase and confirms that Co was successfully incorporated via the flux doping technique. The microstructure of all Sn-58Bi-xCo solders did not differ significantly from Sn-58Bi solders. Sn-58Bi-2.0Co solder exhibited optimum properties among all compositions, with the highest UTS (87.89 ± 2.55 MPa) at 0.01 s−1 strain rate and the lowest IMC layer thickness at the interface after being thermally aged for 56 days (3.84 ± 0.67 µm).

Originality/value

The originality and value of this research lie in its novel exploration of the flux doping technique to introduce minor alloying of Co into Sn-58Bi solder alloys, providing new insights into enhancing the properties and performance of these solders. This new Sn-Bi-Co alloy has the potential to replace lead-containing solder alloy in low-temperature soldering.

Details

Soldering & Surface Mount Technology, vol. 36 no. 5
Type: Research Article
ISSN: 0954-0911

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Article
Publication date: 15 August 2023

Yi-Chung Hu

Tourism demand forecasting is vital for the airline industry and tourism sector. Combination forecasting has the advantage of fusing several forecasts to reduce the risk of…

114

Abstract

Purpose

Tourism demand forecasting is vital for the airline industry and tourism sector. Combination forecasting has the advantage of fusing several forecasts to reduce the risk of inappropriate model selection for analyzing decisions. This paper investigated the effects of a time-varying weighting strategy on the performance of linear and nonlinear forecast combinations in the context of tourism.

Design/methodology/approach

This study used grey prediction models, which did not require that the available data satisfy statistical assumptions, to generate forecasts. A quality-control technique was applied to determine when to change the combination weights to generate combined forecasts by using linear and nonlinear methods.

Findings

The empirical results showed that except for when the Choquet fuzzy integral was used, forecast combination with time-varying weights did not significantly outperform that with fixed weights. The Choquet integral with time-varying weights significantly outperformed that with fixed weights for all model combinations, and had a superior forecasting accuracy to those of other combination methods.

Practical implications

The tourism sector can benefit from the use of the Choquet integral with time-varying weights, by using it to formulate suitable strategies for tourist destinations.

Originality/value

Combining forecasts with time-varying weights may improve the accuracy of the predictions. This study investigated incorporating a time-varying weighting strategy into combination forecasting by using CUSUM. The results verified the effectiveness of the time-varying Choquet integral for tourism forecast combination.

Details

Grey Systems: Theory and Application, vol. 13 no. 4
Type: Research Article
ISSN: 2043-9377

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Article
Publication date: 29 November 2018

Kung Wong Lau, Pui Yuen Lee and Yan Yi Chung

Organizational learning is traditionally structured with conventional in-house learning models aiming to equip employees with practical skills for operational needs. In contrast…

4111

Abstract

Purpose

Organizational learning is traditionally structured with conventional in-house learning models aiming to equip employees with practical skills for operational needs. In contrast, contemporary goals emphasize unstructured organizational learning provided with learning environments to facilitate employees’ formal and informal knowledge creation. Therefore, the conventional organizational learning models are facing tremendous challenges, and it is crucial to change the traditional modes of practice into a new approach of collective learning and knowledge transfer. As well, the emergence of innovative business environments and tacit knowledge-based society urges a new form of organizational learning model to cope with employees’ learning, knowledge transfer and even knowledge management. The paper aims to discuss these issues.

Design/methodology/approach

In this study, the authors’ team applied a typological review for systematically analyzing current organizational learning models aiming to modify and create a new collective model.

Findings

The new model covers the strengths of existing approaches from which the fundamental 3Ps (i.e. principles, purposes and processes of organizational learning) concept is derived from incorporating a development perspective of organizational trajectories and technological innovations.

Originality/value

The authors envisage that the new model can facilitate organizations to assess and adapt their organizational learning needs and orientations by applying this organic and dynamic model which emphasizes assessment in relation to the competitive environment, technological trends and organizational growth.

Details

Leadership & Organization Development Journal, vol. 40 no. 1
Type: Research Article
ISSN: 0143-7739

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Article
Publication date: 11 June 2021

Yi-Chung Cheng, Hui-Chi Chuang and Chih-Chuan Chen

Among the research studies related to the relevance between religious belief and mental health, most of them highlight people with religious belief who tend to obtain mental…

261

Abstract

Purpose

Among the research studies related to the relevance between religious belief and mental health, most of them highlight people with religious belief who tend to obtain mental comforting more easily. However, the research studies mentioned above were cross-sectional studies, and they only verified that religious beliefs and mental health are relevant, but they did not prove their cause-and-effect relationship. That is, they do not identify “due to people's religious beliefs, they have healthier mind” or “due to people's healthier minds, they have religious beliefs.” Therefore, the study aims to explore the benefit evaluation of religious belief affecting mental health.

Design/methodology/approach

The study uses propensity score matching (PSM) and treatment effect (ATT) to carry out the causal inference between religious beliefs and mental health. First, the propensity score (PS) is calculated from the impact factors that affect people's religious belief before establishing counterfactual analysis based on the PS to analyze the effect of religious beliefs to further understand the difference of mental health index between people with religious belief and without it, and confirm the cause-and-effect relationship between them.

Findings

Religious beliefs and participation are ubiquitous within and across populations. The associates between religious participation and health are considerably in great magnitude. Most of the research in the past related to religious beliefs and mental health only verified that religious beliefs and mental health are relevant but not proved its cause-and-effect relationship. This paper aimed to explore the causal relationship between religious belief and mental health. The experimental results showed religious belief has treatment effect toward “daily functioning,” “feeling affect,” “spirituality” and “mental health.” On a whole, religious belief can promote mental health.

Originality/value

In academic and practical circles, there are a lot of research studies exploring the relationship between religious belief and mental health. However, there is no research investigating the cause-and-effect relationship between religious belief and mental health. It also causes some questioning toward the relevant research studies. Therefore, the outcome of this study not only can clarify the legitimacy, importance, and practicality on the researches in the past but also provide the practical support for psychology and counseling.

Details

Kybernetes, vol. 51 no. 7
Type: Research Article
ISSN: 0368-492X

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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 distance…

149

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

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