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首页> 外文期刊>International journal of forecasting >Probabilistic electricity price forecasting with NARX networks: Combine point or probabilistic forecasts?
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Probabilistic electricity price forecasting with NARX networks: Combine point or probabilistic forecasts?

机译：使用NARX网络的概率电价预测：结合点还是概率预测？

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Recent electricity price forecasting studies have shown that decomposing a series of spot prices into a long-term trend-seasonal and a stochastic component, modeling them independently and then combining their forecasts, can yield more accurate point predictions than an approach in which the same regression or neural network model is calibrated to the prices themselves. Here, considering two novel extensions of this concept to probabilistic forecasting, we find that (i) efficiently calibrated non-linear autoregressive with exogenous variables (NARX) networks can outperform their autoregressive counterparts, even without combining forecasts from many runs, and that (ii) in terms of accuracy it is better to construct probabilistic forecasts directly from point predictions. However, if speed is a critical issue, running quantile regression on combined point forecasts (i.e., committee machines) may be an option worth considering. Finally, we confirm an earlier observation that averaging probabilities outperforms averaging quantiles when combining predictive distributions in electricity price forecasting. (C) 2019 International Institute of Forecasters. Published by Elsevier B.V. All rights reserved.
机译：最近的电价预测研究表明，将一系列现货价格分解为长期趋势，季节性和随机因素，分别进行建模，然后组合其预测，可以得到比采用相同回归方法更准确的点预测。或神经网络模型已根据价格本身进行了校准。在这里，考虑到此概念对概率预测的两个新颖扩展，我们发现（i）即使没有组合多次运行的预测，使用外生变量（NARX）网络进行有效校准的非线性自回归也可以胜过自回归对应项，并且（ii ）就准确性而言，最好直接从点预测中构造概率预测。但是，如果速度是一个关键问题，则可以考虑在组合点预测（即委员会机器）上进行分位数回归。最后，我们确认了较早的观察结果，即在电价预测中结合预测分布时，平均概率胜于平均分位数。 （C）2019国际预报员协会。由Elsevier B.V.发布。保留所有权利。

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