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Article
Publication date: 7 April 2015

Zhou Cheng and Tao Juncheng

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…

Abstract

Purpose

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.

Design/methodology/approach

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.

Findings

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.

Originality/value

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.

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Article
Publication date: 27 June 2019

Yinhua Liu, Shiming Zhang and Guoping Chu

This paper aims to present a combination modeling method using multi-source information in the process to improve the accuracy of the dimension propagation relationship…

Abstract

Purpose

This paper aims to present a combination modeling method using multi-source information in the process to improve the accuracy of the dimension propagation relationship for assembly variation reduction.

Design/methodology/approach

Based on a variable weight combination prediction method, the combination model that takes the mechanism model and data-driven model based on inspection data into consideration is established. Furthermore, the combination model is applied to qualification rate prediction for process alarming based on the Monte Carlo simulation and also used in engineering tolerance confirmation in mass production stage.

Findings

The combination model of variable weights considers both the static theoretical mechanic variation propagation model and the dynamic variation relationships from the regression model based on data collections, and provides more accurate assembly deviation predictions for process alarming.

Originality/value

A combination modeling method could be used to provide more accurate variation predictions and new engineering tolerance design procedures for the assembly process.

Details

Assembly Automation, vol. 39 no. 4
Type: Research Article
ISSN: 0144-5154

Keywords

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Book part
Publication date: 29 February 2008

Massimo Guidolin and Carrie Fangzhou Na

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…

Abstract

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.

Details

Forecasting in the Presence of Structural Breaks and Model Uncertainty
Type: Book
ISBN: 978-1-84950-540-6

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Article
Publication date: 20 October 2011

Huayou Chen, Lei Jin, Xiang Li and Mengjie Yao

The purpose of this paper is to propose the optimal combination forecasting model based on closeness degree and induced ordered weighted harmonic averaging (IOWHA…

Abstract

Purpose

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.

Design/methodology/approach

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.

Findings

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

Practical implications

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.

Originality/value

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.

Details

Grey Systems: Theory and Application, vol. 1 no. 3
Type: Research Article
ISSN: 2043-9377

Keywords

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Article
Publication date: 16 July 2021

Muhammad AsadUllah, Muhammad Adnan Bashir and Abdur Rahman Aleemi

The purpose of this study is to examine the accuracy of combined models with the individual models in terms of forecasting Euro against US dollar during COVID-19 era…

Abstract

Purpose

The purpose of this study is to examine the accuracy of combined models with the individual models in terms of forecasting Euro against US dollar during COVID-19 era. During COVID, the euro shows sharp fluctuation in upward and downward trend; therefore, this study is keen to find out the best-fitted model which forecasts more accurately during the pandemic.

Design/methodology/approach

The descriptive design has been adopted in this research. The three univariate models, i.e. autoregressive integrated moving averages (ARIMA), Naïve, exponential smoothing (ES) model, and one multivariate model, i.e. nonlinear autoregressive distributive lags (NARDL), are selected to forecast the exchange rate of Euro against the US dollar during the COVID. The above models are combined via equal weights and var-cor methods to find out the accuracy of forecasting as Poon and Granger (2003) showed that combined models can forecast better than individual models.

Findings

NARDL outperforms all remaining individual models, i.e. ARIMA, Naïve and ES. By applying a combination of different models via different techniques, the combination of NARDL and Naïve models outperforms all combination of models by scoring the least mean absolute percentage error value, i.e. 1.588. The combined forecasting of NARDL and Naïve techniques under var-cor method also outperforms the forecasting accuracy of individual models other than NARDL. It means the euro exchange rate against the US dollar which is dependent upon the macroeconomic fundamentals and recent observations of the time series.

Practical implications

The findings could help the FOREX market, hedgers, traders, businessmen, policymakers, economists, financial managers, etc., to minimize the risk indulged in global trade. It also helps to produce more accurate results in different financial models, i.e. capital asset pricing model and arbitrage pricing theory, because their findings may not be useful if exchange rate fluctuations do not trace effectively.

Originality/value

The NARDL models have been applied previously in different time series and only limited to the asymmetric or symmetric relationships. This study is using it for the forecasting exchange rate which is almost abandoned in earlier literature. Furthermore, this study combined the NARDL with univariate models to produce the accuracy which itself is a novelty. Moreover, the findings help to enhance the effectiveness of different financial theories as well.

Details

foresight, vol. ahead-of-print no. ahead-of-print
Type: Research Article
ISSN: 1463-6689

Keywords

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Book part
Publication date: 1 January 2008

Michael K. Andersson and Sune Karlsson

We consider forecast combination and, indirectly, model selection for VAR models when there is uncertainty about which variables to include in the model in addition to the…

Abstract

We consider forecast combination and, indirectly, model selection for VAR models when there is uncertainty about which variables to include in the model in addition to the forecast variables. The key difference from traditional Bayesian variable selection is that we also allow for uncertainty regarding which endogenous variables to include in the model. That is, all models include the forecast variables, but may otherwise have differing sets of endogenous variables. This is a difficult problem to tackle with a traditional Bayesian approach. Our solution is to focus on the forecasting performance for the variables of interest and we construct model weights from the predictive likelihood of the forecast variables. The procedure is evaluated in a small simulation study and found to perform competitively in applications to real world data.

Details

Bayesian Econometrics
Type: Book
ISBN: 978-1-84855-308-8

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Book part
Publication date: 17 November 2010

Joanne S. Utley and J. Gaylord May

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…

Abstract

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.

Details

Advances in Business and Management Forecasting
Type: Book
ISBN: 978-0-85724-201-3

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Article
Publication date: 4 April 2008

Drago Podobnik and Slavko Dolinšek

The purpose of this paper is to present the usefulness of the combination of the European Foundation for Quality Management excellence model and the balanced scorecard…

Abstract

Purpose

The purpose of this paper is to present the usefulness of the combination of the European Foundation for Quality Management excellence model and the balanced scorecard integrated into the management model for competitiveness and performance development.

Design/methodology/approach

The presented model is the result of a business research where comparative analysis of the two models has been carried out. Both models have been thoroughly studied from different points of view. Such an approach enabled one to define the strengths, weaknesses and similarities of the two models.

Findings

On the basis of the robustness of both models a combination was formed integrated into the management model, which is likely to be better, more effective and simpler to use in practice, and which will support an increase in competitiveness and performance development.

Research limitations/implications

Within the research the aim has been focused on close research into interactions presented in the integrated management model. Throughout research, consideration was given to the problem of its external validity which is somehow limited. Here analytical generalisation is discussed. A number of cases can be found in which a combination of both models has been used in different ways. The way of combination in the integral management model, which is presented here, was carried out in 2005 in an important Slovenian international company.

Originality/value

The originality can be found in the particular approach towards comparative analysis and also in the result, which represents a combination of the two models integrated in an original manner into the integral model of management. Companies which have not yet introduced, as well as those which have already introduced, one of the researched management tools will be able to use the results of this research for further upgrading/consolidation in the sense of the model combination of both. The synergetic effects of the interactions of the combination between both management approaches will have a positive effect on increasing company competitiveness.

Details

Journal of Organizational Change Management, vol. 21 no. 2
Type: Research Article
ISSN: 0953-4814

Keywords

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Book part
Publication date: 13 March 2013

Joanne Utley

Past research has shown that forecast combination typically improves demand forecast accuracy even when only two component forecasts are used; however, systematic bias in…

Abstract

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.

Details

Advances in Business and Management Forecasting
Type: Book
ISBN: 978-1-78190-331-5

Keywords

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Article
Publication date: 1 February 1998

EDIZ ALKOC and FUAT ERBATUR

Computer simulation in construction planning has been the subject of research for the last few decades. The present paper describes simulation models geared toward…

Abstract

Computer simulation in construction planning has been the subject of research for the last few decades. The present paper describes simulation models geared toward improving the productivity of concreting operations. It is primarily concerned with a study of the sensitivity of concreting operations to a set of possible resource combinations. Thirteen models are examined by the two well‐known methods of concreting: (1) crane and bucket; and (2) the pump. Concreting into slabs, beams and columns are considered. The simulation software Micro‐CYCLONE is used for the actual generation of models. Sensitivity parameters considered in resource combinations include the number of truck‐mixers, buckets and labourers in concrete placing crews. The simulation models developed are compared and the results are discussed. The results enable planners to realize how the resource quantities and capacities in one cycle affect the ones in another period for cyclic operations like concreting. It can be concluded that the maximum number of resources, the interaction of work crews caused by work space limitations and the interaction of equipment because of sharing with other activities of the project may bring limitations.

Details

Engineering, Construction and Architectural Management, vol. 5 no. 2
Type: Research Article
ISSN: 0969-9988

Keywords

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