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摘要翻译：
本文包括电价预测的依赖结构和预测评价。我们从德国-奥地利日前价格的非峰值和峰值时间序列中工作，因此我们分析了二元数据。我们首先估计两个时间序列的均值，然后在第二步中估计残差。用最小二乘法和弹性网估计平均方程，用极大似然估计残差。我们的贡献是在均值回复跳跃扩散模型的残差中包含了一个二元跳跃分量。模型的预测使用四个不同的标准进行评估，包括能量得分来衡量时间序列之间的相关结构是否被适当地包括在内。结果表明，具有双变量跳跃的模型具有更好的能量得分结果，这意味着考虑这种结构对于正确预测相关时间序列是非常重要的。
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英文标题：
《Probabilistic Forecasting in Day-Ahead Electricity Markets: Simulating
Peak and Off-Peak Prices》
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作者：
Peru Muniain and Florian Ziel
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最新提交年份：
2019
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分类信息：
一级分类：Economics 经济学
二级分类：Econometrics 计量经济学
分类描述：Econometric Theory, Micro-Econometrics, Macro-Econometrics, Empirical Content of Economic Relations discovered via New Methods, Methodological Aspects of the Application of Statistical Inference to Economic Data.
计量经济学理论，微观计量经济学，宏观计量经济学，通过新方法发现的经济关系的实证内容，统计推论应用于经济数据的方法论方面。
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英文摘要：
In this paper we include dependency structures for electricity price forecasting and forecasting evaluation. We work with off-peak and peak time series from the German-Austrian day-ahead price, hence we analyze bivariate data. We first estimate the mean of the two time series, and then in a second step we estimate the residuals. The mean equation is estimated by OLS and elastic net and the residuals are estimated by maximum likelihood. Our contribution is to include a bivariate jump component on a mean reverting jump diffusion model in the residuals. The models' forecasts are evaluated using four different criteria, including the energy score to measure whether the correlation structure between the time series is properly included or not. In the results it is observed that the models with bivariate jumps provide better results with the energy score, which means that it is important to consider this structure in order to properly forecast correlated time series.
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PDF链接：
https://arxiv.org/pdf/1810.08418
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