| 1. |
- Dimoulkas, Ilias, et al.
(författare)
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Neural networks for GEFCom2017 probabilistic load forecasting
- 2019
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Ingår i: International Journal of Forecasting. - : Elsevier BV. - 0169-2070 .- 1872-8200. ; 35:4, s. 1409-1423
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Tidskriftsartikel (refereegranskat)abstract
- This report describes the forecasting model which was developed by team "4C" for the global energy forecasting competition 2017 (GEFCom2017), with some modifications added afterwards to improve its accuracy. The model is based on neural networks. Temperature scenarios obtained from historical data are used as inputs to the neural networks in order to create load scenarios, and these load scenarios are then transformed into quantiles. By using a feature selection approach that is based on a stepwise regression technique, a neural network based model is developed for each zone. Furthermore, a dynamic choice of the temperature scenarios is suggested. The feature selection and dynamic choice of the temperature scenarios can improve the quantile scores considerably, resulting in very accurate forecasts among the top teams.
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| 2. |
- Kourentzes, Nikolaos
(författare)
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Demand Forecasting for Managers
- 2018
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Ingår i: International Journal of Forecasting. - : Elsevier. - 0169-2070 .- 1872-8200. ; 34:1, s. 117-118
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Recension (övrigt vetenskapligt/konstnärligt)
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| 3. |
- Sahamkhadam, Maziar, et al.
(författare)
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Portfolio optimization based on GARCH-EVT-Copula forecasting models
- 2018
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Ingår i: International Journal of Forecasting. - : Elsevier. - 0169-2070 .- 1872-8200. ; 34:3, s. 497-506
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Tidskriftsartikel (refereegranskat)abstract
- This study uses GARCH-EVT-copula and ARMA-GARCH-EVT-copula models to perform out-of-sample forecasts and simulate one-day-ahead returns for ten stock indexes. We construct optimal portfolios based on the global minimum variance (GMV), minimum conditional value-at-risk (Min-CVaR) and certainty equivalence tangency (CET) criteria, and model the dependence structure between stock market returns by employing elliptical (Student-t and Gaussian) and Archimedean (Clayton, Frank and Gumbel) copulas. We analyze the performances of 288 risk modeling portfolio strategies using out-of-sample back-testing. Our main finding is that the CET portfolio, based on ARMA-GARCH-EVT-copula forecasts, outperforms the benchmark portfolio based on historical returns. The regression analyses show that GARCH-EVT forecasting models, which use Gaussian or Student-t copulas, are best at reducing the portfolio risk. (C) 2018 International Institute of Forecasters. Published by Elsevier B.V. All rights reserved.
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| 4. |
- Schaer, Oliver, et al.
(författare)
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Demand forecasting with user-generated online information
- 2019
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Ingår i: International Journal of Forecasting. - : Elsevier. - 0169-2070 .- 1872-8200. ; 35:1, s. 197-212
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Tidskriftsartikel (refereegranskat)abstract
- Recently, there has been substantial research on the augmentation of aggregate forecasts with individual consumer data from internet platforms, such as search traffic or social network shares. Although the majority of studies have reported increases in accuracy, many exhibit design weaknesses, including a lack of adequate benchmarks or rigorous evaluation. Furthermore, their usefulness over the product life-cycle has not been investigated, even though this may change, as consumers may search initially for pre-purchase information, but later for after-sales support. This study begins by reviewing the relevant literature, then attempts to support the key findings using two forecasting case studies. Our findings are in stark contrast to those in the previous literature, as we find that established univariate forecasting benchmarks, such as exponential smoothing, consistently perform better those that include online information. Our research underlines the need for a thorough forecast evaluation and argues that the usefulness of online platform data for supporting operational decisions may be limited.
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| 5. |
- Trapero, Juan R., et al.
(författare)
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Quantile forecast optimal combination to enhance safety stock estimation
- 2019
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Ingår i: International Journal of Forecasting. - : Elsevier. - 0169-2070 .- 1872-8200. ; 35:1, s. 239-250
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Tidskriftsartikel (refereegranskat)abstract
- The safety stock calculation requires a measure of the forecast error uncertainty. Such errors are usually assumed to be Gaussian iid (independently and identically distributed). However, deviations from iid lead to a deterioration in the performance of the supply chain. Recent research has shown that, contrary to theoretical approaches, empirical techniques that do not rely on the aforementioned assumptions can enhance the calculation of safety stocks. In particular, GARCH models cope with time-varying heterocedastic forecast error, and kernel density estimation does not need to rely on a determined distribution. However, if the forecast errors are time-varying heterocedastic and do not follow a determined distribution, the previous approaches are inadequate. We overcome this by proposing an optimal combination of the empirical methods that minimizes the asymmetric piecewise linear loss function, also known as the tick loss. The results show that combining quantile forecasts yields safety stocks with a lower cost. The methodology is illustrated with simulations and real data experiments for different lead times.
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| 6. |
- Westerlund, Joakim, et al.
(författare)
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Testing for predictability in panels of any time series dimension
- 2016
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Ingår i: International Journal of Forecasting. - : Elsevier BV. - 1872-8200 .- 0169-2070. ; 32:4, s. 1162-1177
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Tidskriftsartikel (refereegranskat)abstract
- The few panel data tests for predictability of returns that exist are based on the prerequisite that both the number of time series observations, $T$, and the number of cross-section units, $N$, are large. As a result, these tests are impossible for stock markets where lengthy time series data are scarce. In response to this, the current paper develops a new test for predictability in panels where $N$ is large and $T \geq 2$ can be small or large, or indeed anything in between the two extremes. This consideration represents an advancement when compared to the usual large-$N$ and large-$T$ requirement. The new test is also very general, especially when it comes to the allowable predictors, and it is easy to implement. As an illustration, we consider the Chinese stock market, for which data is only available for 17 years but where the number firms is relatively large, 160.
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