Putting Big Data analytics to work: Feature selection for forecasting electricity prices using the LASSO and random forests
Putting Big Data analytics to work: Feature selection for forecasting electricity prices using the LASSO and random forests
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DOI：
10.1080/12460125.2015.994290
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发表时间：
2015-01-01
影响因子：
3.4
通讯作者：
Neumann, Dirk
中科院分区：
文献类型：
--
作者：
Ludwig, Nicole;Feuerriegel, Stefan;Neumann, Dirk
成功的公司越来越多地是那些擅长从数据中提取知识的公司。挖掘“大数据”的来源需要强大的算法，以及对所使用数据的深刻理解。预测分析的关键挑战之一是识别可能解释感兴趣变量的相关因素。在本文中，我们提出了一个预测分析的案例研究，我们专注于选择相关的外生变量。更具体地说，我们试图预测德国电力现货价格参考历史价格和一组深刻的天气变量。为了选择相关的气象站，我们使用最小绝对收缩选择操作（LASSO）和随机森林隐式执行变量选择。总的来说，在我们对德国天气数据的案例研究中，我们设法将预测准确率提高了16.9%。
Successful companies are increasingly those companies that excel in the task of extracting knowledge from data. Tapping the source of 'Big Data' requires powerful algorithms combined with a strong understanding of the data used. One of the key challenges in predictive analytics is the identification of relevant factors that may explain the variables of interest. In this paper, we present a case study in predictive analytics in which we focus on the selection of relevant exogenous variables. More specifically, we attempt to predict the German electricity spot prices with reference to historical prices and a deep set of weather variables. In order to choose the relevant weather stations, we use the least absolute shrinkage selection operation (LASSO) and random forests to implicitly execute a variable selection. Overall, in our case study of German weather data, we manage to improve forecasting accuracy by up to 16.9% in terms of mean average error.