英文标题：
《Forecasting Electricity Spot Prices using Lasso: On Capturing the
Autoregressive Intraday Structure》
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作者：
Florian Ziel
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最新提交年份：
2016
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英文摘要：
In this paper we present a regression based model for day-ahead electricity spot prices. We estimate the considered linear regression model by the lasso estimation method. The lasso approach allows for many possible parameters in the model, but also shrinks and sparsifies the parameters automatically to avoid overfitting. Thus, it is able to capture the autoregressive intraday dependency structure of the electricity price well. We discuss in detail the estimation results which provide insights to the intraday behavior of electricity prices. We perform an out-of-sample forecasting study for several European electricity markets. The results illustrate well that the efficient lasso based estimation technique can exhibit advantages from two popular model approaches.
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中文摘要：
在本文中，我们提出了一个基于回归的日前电力现货价格模型。我们用lasso估计方法估计所考虑的线性回归模型。套索方法允许模型中有许多可能的参数，但也会自动缩小和稀疏参数，以避免过度拟合。因此，它能够很好地捕捉电价的自回归日内依赖结构。我们详细讨论了估算结果，这些结果为电价的日内行为提供了见解。我们对几个欧洲电力市场进行了抽样预测研究。结果表明，基于套索的有效估计技术可以从两种流行的模型方法中表现出优势。
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分类信息：

一级分类：Quantitative Finance 数量金融学
二级分类：Statistical Finance 统计金融
分类描述：Statistical, econometric and econophysics analyses with applications to financial markets and economic data
统计、计量经济学和经济物理学分析及其在金融市场和经济数据中的应用
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