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The purpose of this paper is to propose the optimal combination forecasting model based on closeness degree and induced ordered weighted harmonic averaging (IOWHA) operator under the uncertain environment in which the raw data are provided by interval numbers.

Starting from maximizing the closeness degree of combination forecasting, which is different from minimizing absolute errors, weighted coefficient vectors of combination forecasting methods are obtained. The new concepts of closeness degree for the center and radius of interval numbers sequences are put forward and the optimal interval combination forecasting model is constructed by maximizing the sum of convex combination with closeness degree of interval center and closeness degree of interval radius. The solution to the model is discussed.

The results show that this model can improve the combination forecasting accuracy efficiently compared with that of each single forecasting method.

The method proposed in the paper can be used to forecast future tendency in a wide ranges of fields, such as engineering, economics and management. In particular, the raw data are provided in the form of interval numbers under the uncertain environment.

The combination forecasting model proposed in this paper is based on closeness degree and IOWHA operator, which is a new kind of combination forecasting model with variant weights.

We address an interesting case – the predictability of excess US asset returns from macroeconomic factors within a flexible regime-switching VAR framework – in which the presence of regimes may lead to superior forecasting performance from forecast combinations. After documenting that forecast combinations provide gains in predictive accuracy and that these gains are statistically significant, we show that forecast combinations may substantially improve portfolio selection. We find that the best-performing forecast combinations are those that either avoid estimating the pooling weights or that minimize the need for estimation. In practice, we report that the best-performing combination schemes are based on the principle of relative past forecasting performance. The economic gains from combining forecasts in portfolio management applications appear to be large, stable over time, and robust to the introduction of realistic transaction costs.

The authors develop a novel forecast combination approach based on the order statistics of individual predictability from panel data forecasts. To this end, the authors define the notion of forecast depth, which provides a ranking among different forecasts based on their normalized forecast errors during the training period. The forecast combination is in the form of a depth-weighted trimmed mean. The authors derive the limiting distribution of the depth-weighted forecast combination, based on which the authors can readily construct prediction intervals. Using this novel forecast combination, the authors predict the national level of new COVID-19 cases in the United States and compare it with other approaches including the ensemble forecast from the Centers for Disease Control and Prevention (CDC). The authors find that the depth-weighted forecast combination yields more accurate and robust predictions compared with other popular forecast combinations and reports much narrower prediction intervals.

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.

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.

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.

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.

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.

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.

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.

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

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.

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.

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.

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.

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.

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.

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.

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。

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。

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 CUGM 與 CAGM 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。

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。

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。

旅遊需求,涵蓋性檢定,Google 搜尋趨勢,灰色預測,組合預測

研究型论文

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.

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.

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.

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.

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.

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

Trabajo de investigación

To accurately forecast logistics freight volume plays a vital part in rational planning formulation for a country. The purpose of this paper is to contribute to developing a novel combination forecasting model to predict China’s logistics freight volume, in which an improved PSO-BP neural network is proposed to determine the combination weights.

Since BP neural network has the ability of learning, storing, and recalling information that given by individual forecasting models, it is effective in determining the combination weights of combination forecasting model. First, an improved PSO based on simulated annealing method and space-time adjustment strategy (SAPSO) is proposed to solve out the connection weights of BP neural network, which overcomes the problems of local optimum traps, low precision and poor convergence during BP neural network training process. Then, a novel combination forecast model based on SAPSO-BP neural network is established.

Simulation tests prove that the proposed SAPSO has better convergence performance and more stability. At the same time, combination forecasting models based on three types of BP neural networks are developed, which rank as SAPSO-BP, PSO-BP and BP in accordance with mean absolute percentage error (MAPE) and convergent speed. Also the proposed combination model based on SAPSO-BP shows its superiority, compared with some other combination weight assignment methods.

SAPSO-BP neural network is an original contribution to the combination weight assignment methods of combination forecasting model, which has better convergence performance and more stability.

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.

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.

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.

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.

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

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。

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。

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。

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。

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。

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.

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.

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.

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.

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.

Past research has shown that forecast combination typically improves demand forecast accuracy even when only two component forecasts are used; however, systematic bias in the component forecasts can reduce the effectiveness of combination. This study proposes a methodology for combining demand forecasts that are biased. Data from an actual manufacturing shop are used to develop the methodology and compare its accuracy with the accuracy of the standard approach of correcting for bias prior to combination. Results indicate that the proposed methodology outperforms the standard approach.

This study examines the use of forecast combination to improve the accuracy of forecasts of cumulative demand. A forecast combination methodology based on least absolute value (LAV) regression analysis is developed and is applied to partially accumulated demand data from an actual manufacturing operation. The accuracy of the proposed model is compared with the accuracy of common alternative approaches that use partial demand data. Results indicate that the proposed methodology outperforms the alternative approaches.