Econometric Forecasting Models Based on Forecast Combination Methods

2018 ◽  
Author(s):  
Vera Ivanyuk
Keyword(s):  
Forecast Combination ◽  
Forecasting Models ◽  
Combination Methods

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.

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.

Forecast combination and the Bank of England's suite of statistical forecasting models

2008 ◽  
Vol 25 (4) ◽  
pp. 772-792 ◽  
Author(s):  
George Kapetanios ◽  
Vincent Labhard ◽  
Simon Price
Keyword(s):  
Forecast Combination ◽  
Statistical Forecasting ◽  
Forecasting Models

scholarly journals Optimal forecasting accuracy using Lp-norm combination

METRON ◽  
2021 ◽  
Author(s):  
Massimiliano Giacalone
Keyword(s):  
Real Data ◽  
Optimization Techniques ◽  
Combination Method ◽  
Forecast Combination ◽  
Forecasting Accuracy ◽  
Lp Norm ◽  
Combination Methods ◽  
Historical Series ◽  
Non Gaussian ◽  
Standard Regression

AbstractA well-known result in statistics is that a linear combination of two-point forecasts has a smaller Mean Square Error (MSE) than the two competing forecasts themselves (Bates and Granger in J Oper Res Soc 20(4):451–468, 1969). The only case in which no improvements are possible is when one of the single forecasts is already the optimal one in terms of MSE. The kinds of combination methods are various, ranging from the simple average (SA) to more robust methods such as the one based on median or Trimmed Average (TA) or Least Absolute Deviations or optimization techniques (Stock and Watson in J Forecast 23(6):405–430, 2004). Standard regression-based combination approaches may fail to get a realistic result if the forecasts show high collinearity in several situations or the data distribution is not Gaussian. Therefore, we propose a forecast combination method based on Lp-norm estimators. These estimators are based on the Generalized Error Distribution, which is a generalization of the Gaussian distribution, and they can be used to solve the cases of multicollinearity and non-Gaussianity. In order to demonstrate the potential of Lp-norms, we conducted a simulated and an empirical study, comparing its performance with other standard-regression combination approaches. We carried out the simulation study with different values of the autoregressive parameter, by alternating heteroskedasticity and homoskedasticity. On the other hand, the real data application is based on the daily Bitfinex historical series of bitcoins (2014–2020) and the 25 historical series relating to companies included in the Dow Jonson, were subsequently considered. We showed that, by combining different GARCH and the ARIMA models, assuming both Gaussian and non-Gaussian distributions, the Lp-norm scheme improves the forecasting accuracy with respect to other regression-based combination procedures.

scholarly journals A Forecast Combination Framework with Multi-Time Scale for Livestock Products’ Price Forecasting

2019 ◽  
Vol 2019 ◽  
pp. 1-11
Author(s):  
Liwen Ling ◽  
Dabin Zhang ◽  
Amin W. Mugera ◽  
Shanying Chen ◽  
Qiang Xia
Keyword(s):  
Time Scale ◽  
Geometric Mean ◽  
Forecast Combination ◽  
Short Term ◽  
Forecast Performance ◽  
Short Term Forecast ◽  
Superior Result ◽  
Combination Methods ◽  
Term Forecast ◽  
Livestock Products

China’s livestock market has experienced exceptionally severe price fluctuations over the past few years. In this paper, based on the well-established idea of “forecast combination,” a forecast combination framework with different time scales is proposed to improve the forecast accuracy for livestock products. Specifically, we combine the forecasts from multi-time scale, i.e., the short-term forecast and the long-term forecast. Forecasts derived from multi-time scale introduce complementary information about the dynamics of price movements, thus increasing the diversities within the modeling process. Moreover, we investigate a total of ten combination methods with different weighting schemes, including linear and nonlinear combination. The empirical results show that (i) forecast performance can be remarkably improved with this novel combination idea, and short-term forecast model is more suitable for the products with a relatively high volatility, e.g., mutton and beef; (ii) geometric mean, which provides a nonlinear combination, is the most effective one among all the combination methods; and (iii) variance-based weighting scheme can yield a superior result compared to the best individual forecast, especially for the products such as egg and beef.

scholarly journals Impact of Information Sharing and Forecast Combination on Fast-Moving-Consumer-Goods Demand Forecast Accuracy

Information ◽  
2019 ◽  
Vol 10 (8) ◽  
pp. 260 ◽  
Author(s):  
Dazhi Yang ◽  
Allan N. Zhang
Keyword(s):  
Supply Chain ◽  
Consumer Goods ◽  
Business Strategy ◽  
Fast Moving ◽  
A Priori ◽  
Forecast Accuracy ◽  
Forecast Combination ◽  
Demand Forecast ◽  
Combination Methods ◽  
Fast Moving Consumer Goods

This article empirically demonstrates the impacts of truthfully sharing forecast information and using forecast combinations in a fast-moving-consumer-goods (FMCG) supply chain. Although it is known a priori that sharing information improves the overall efficiency of a supply chain, information such as pricing or promotional strategy is often kept proprietary for competitive reasons. In this regard, it is herein shown that simply sharing the retail-level forecasts—this does not reveal the exact business strategy, due to the effect of omni-channel sales—yields nearly all the benefits of sharing all pertinent information that influences FMCG demand. In addition, various forecast combination methods are used to further stabilize the forecasts, in situations where multiple forecasting models are used during operation. In other words, it is shown that combining forecasts is less risky than “betting” on any component model.

scholarly journals Day Ahead Hourly Global Horizontal Irradiance Forecasting—Application to South African Data

Energies ◽  
2019 ◽  
Vol 12 (18) ◽  
pp. 3569 ◽  
Author(s):  
Phathutshedzo Mpfumali ◽  
Caston Sigauke ◽  
Alphonce Bere ◽  
Sophie Mulaudzi
Keyword(s):  
Machine Learning ◽  
Solar Power ◽  
Prediction Interval ◽  
Combination Method ◽  
Support Vector ◽  
Forecast Combination ◽  
Learning Methods ◽  
Machine Learning Methods ◽  
Stochastic Gradient Boosting ◽  
Combination Methods

Due to its variability, solar power generation poses challenges to grid energy management. In order to ensure an economic operation of a national grid, including its stability, it is important to have accurate forecasts of solar power. The current paper discusses probabilistic forecasting of twenty-four hours ahead of global horizontal irradiance (GHI) using data from the Tellerie radiometric station in South Africa for the period August 2009 to April 2010. Variables are selected using a least absolute shrinkage and selection operator (Lasso) via hierarchical interactions and the parameters of the developed models are estimated using the Barrodale and Roberts’s algorithm. Two forecast combination methods are used in this study. The first is a convex forecast combination algorithm where the average loss suffered by the models is based on the pinball loss function. A second forecast combination method, which is quantile regression averaging (QRA), is also used. The best set of forecasts is selected based on the prediction interval coverage probability (PICP), prediction interval normalised average width (PINAW) and prediction interval normalised average deviation (PINAD). The results demonstrate that QRA gives more robust prediction intervals than the other models. A comparative analysis is done with two machine learning methods—stochastic gradient boosting and support vector regression—which are used as benchmark models. Empirical results show that the QRA model yields the most accurate forecasts compared to the machine learning methods based on the probabilistic error measures. Results on combining prediction interval limits show that the PMis the best prediction limits combination method as it gives a hit rate of 0.955 which is very close to the target of 0.95. This modelling approach is expected to help in optimising the integration of solar power in the national grid.

scholarly journals Short-Term Wind Speed Forecasting Using Statistical and Machine Learning Methods

Algorithms ◽  
2020 ◽  
Vol 13 (6) ◽  
pp. 132 ◽  
Author(s):  
Lucky O. Daniel ◽  
Caston Sigauke ◽  
Colin Chibaya ◽  
Rendani Mbuvha
Keyword(s):  
Wind Speed ◽  
Quantile Regression ◽  
Predictive Performance ◽  
Additive Models ◽  
Combination Method ◽  
Forecast Combination ◽  
Primary Input ◽  
Stochastic Gradient Boosting ◽  
Combination Methods ◽  
Forecasts Combination

Wind offers an environmentally sustainable energy resource that has seen increasing global adoption in recent years. However, its intermittent, unstable and stochastic nature hampers its representation among other renewable energy sources. This work addresses the forecasting of wind speed, a primary input needed for wind energy generation, using data obtained from the South African Wind Atlas Project. Forecasting is carried out on a two days ahead time horizon. We investigate the predictive performance of artificial neural networks (ANN) trained with Bayesian regularisation, decision trees based stochastic gradient boosting (SGB) and generalised additive models (GAMs). The results of the comparative analysis suggest that ANN displays superior predictive performance based on root mean square error (RMSE). In contrast, SGB shows outperformance in terms of mean average error (MAE) and the related mean average percentage error (MAPE). A further comparison of two forecast combination methods involving the linear and additive quantile regression averaging show the latter forecast combination method as yielding lower prediction accuracy. The additive quantile regression averaging based prediction intervals also show outperformance in terms of validity, reliability, quality and accuracy. Interval combination methods show the median method as better than its pure average counterpart. Point forecasts combination and interval forecasting methods are found to improve forecast performance.

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