Forecast Combination and Encompassing

2007 ◽  
pp. 268-283 ◽  
Author(s):  
Paul Newbold ◽  
David I. Harvey
Keyword(s):  
Forecast Combination

Forecast combination with meta possibilistic fuzzy functions

2021 ◽  
Vol 560 ◽  
pp. 168-182
Author(s):  
Nihat Tak
Keyword(s):  
Forecast Combination

An adaptive forecast combination approach based on meta intuitionistic fuzzy functions

2021 ◽  
Vol 40 (5) ◽  
pp. 9567-9581
Author(s):  
Nihat Tak ◽  
Erol Egrioglu ◽  
Eren Bas ◽  
Ufuk Yolcu
Keyword(s):  
Meta Analysis ◽  
Forecast Combination ◽  
Specific Topic ◽  
Fuzzy C Means ◽  
Intuitionistic Fuzzy ◽  
Forecasting Methods ◽  
Combination Approach ◽  
Combination Function ◽  
Fuzzy Forecast

Intuitionistic meta fuzzy forecast combination functions are introduced in the paper. There are two challenges in the forecast combination literature, determining the optimum weights and the methods to combine. Although there are a few studies on determining the methods, there are numerous studies on determining the optimum weights of the forecasting methods. In this sense, the questions like “What methods should we choose in the combination?” and “What combination function or the weights should we choose for the methods” are handled in the proposed method. Thus, the first two contributions that the paper aims to propose are to obtain the optimum weights and the proper forecasting methods in combination functions by employing meta fuzzy functions (MFFs). MFFs are recently introduced for aggregating different methods on a specific topic. Although meta-analysis aims to combine the findings of different primary studies, MFFs aim to aggregate different methods based on their performances on a specific topic. Thus, forecasting is selected as the specific topic to propose a novel forecast combination approach inspired by MFFs in this study. Another contribution of the paper is to improve the performance of MFFs by employing intuitionistic fuzzy c-means. 14 meteorological datasets are used to evaluate the performance of the proposed method. Results showed that the proposed method can be a handy tool for dealing with forecasting problems. The outstanding performance of the proposed method is verified in terms of RMSE and MAPE.

Shipping market forecasting by forecast combination mechanism

2021 ◽  
pp. 1-16
Author(s):  
Ruobin Gao ◽  
Jiahui Liu ◽  
Liang Du ◽  
Kum Fai Yuen
Keyword(s):  
Forecast Combination

A preference-based method for forecast combination

1989 ◽  
Vol 8 (3) ◽  
pp. 269-292 ◽  
Author(s):  
Kent D. Wall ◽  
Charles Correia
Keyword(s):  
Forecast Combination

Forecasting Tourism Demand With a New Time-Varying Forecast Averaging Approach

2021 ◽  
pp. 004728752110612
Author(s):  
Yuying Sun ◽  
Jian Zhang ◽  
Xin Li ◽  
Shouyang Wang
Keyword(s):  
Structural Changes ◽  
Model Averaging ◽  
Structural Instability ◽  
Time Varying ◽  
Forecast Combination ◽  
Tourism Demand ◽  
Single Model ◽  
Forecasting Accuracy ◽  
Combination Methods ◽  
Out Of Sample

Existing research has shown that combination can effectively improve tourism forecasting accuracy compared with single model. However, the model uncertainty and structural instability in combination for out-of-sample tourism forecasting may influence the forecasting performance. This paper proposes a novel forecast combination approach based on time-varying jackknife model averaging (TVJMA), which can more efficiently handle structural changes and nonstationary trends in tourism data. Using Hong Kong tourism demand from five major tourism source regions as an empirical study, we investigate whether our proposed nonparametric TVJMA-based approach can improve tourism forecasting accuracy further. Empirical results show that the proposed TVJMA-based approach outperforms other competitors including single model and three combination methods in most cases. Findings indicate the outstanding performance of our method is robust to various forecasting horizons and different estimation periods.

Reducing complexity in multivariate electricity price forecasting

2021 ◽  
Vol ahead-of-print (ahead-of-print) ◽  
Author(s):  
Hendrik Kohrs ◽  
Benjamin Rainer Auer ◽  
Frank Schuhmacher
Keyword(s):  
Electricity Market ◽  
Model Comparison ◽  
Variable Importance ◽  
Support Vector ◽  
Dynamic Factor ◽  
Forecast Combination ◽  
Content Type ◽  
Electricity Price Forecasting ◽  
Bayesian Shrinkage ◽  
Short Term Forecasting

Purpose In short-term forecasting of day-ahead electricity prices, incorporating intraday dependencies is vital for accurate predictions. However, it quickly leads to dimensionality problems, i.e. ill-defined models with too many parameters, which require an adequate remedy. This study addresses this issue. Design/methodology/approach In an application for the German/Austrian market, this study derives variable importance scores from a random forest algorithm, feeds the identified variables into a support vector machine and compares the resulting forecasting technique to other approaches (such as dynamic factor models, penalized regressions or Bayesian shrinkage) that are commonly used to resolve dimensionality problems. Findings This study develops full importance profiles stating which hours of which past days have the highest predictive power for specific hours in the future. Using the profile information in the forecasting setup leads to very promising results compared to the alternatives. Furthermore, the importance profiles provide a possible explanation why some forecasting methods are more accurate for certain hours of the day than others. They also help to explain why simple forecast combination schemes tend to outperform the full battery of models considered in the comprehensive comparative study. Originality/value With the information contained in the variable importance scores and the results of the extensive model comparison, this study essentially provides guidelines for variable and model selection in future electricity market research.

scholarly journals Probability Forecast Combination via Entropy Regularized Wasserstein Distance

Entropy ◽  
2020 ◽  
Vol 22 (9) ◽  
pp. 929
Author(s):  
Ryan Cumings-Menon ◽  
Minchul Shin
Keyword(s):  
Wasserstein Distance ◽  
Tuning Parameter ◽  
Forecast Combination ◽  
Probability Forecast ◽  
Density Forecast ◽  
Combination Methods ◽  
Multivariate Gaussian ◽  
Density Forecasting ◽  
Gaussian Input

We propose probability and density forecast combination methods that are defined using the entropy regularized Wasserstein distance. First, we provide a theoretical characterization of the combined density forecast based on the regularized Wasserstein distance under the assumption. More specifically, we show that the regularized Wasserstein barycenter between multivariate Gaussian input densities is multivariate Gaussian, and provide a simple way to compute mean and its variance–covariance matrix. Second, we show how this type of regularization can improve the predictive power of the resulting combined density. Third, we provide a method for choosing the tuning parameter that governs the strength of regularization. Lastly, we apply our proposed method to the U.S. inflation rate density forecasting, and illustrate how the entropy regularization can improve the quality of predictive density relative to its unregularized counterpart.

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