We propose a deep learning-based methodology to investigate the complex dynamics of electricity prices observed in power markets. The aims are: (a) to process missing data in power price time series with irregular observation times; (b) to detect a Gaussian component in the log-return empirical distributions if there is one; (c) to define suitable stochastic models of the dynamics of power prices. We apply this methodology to US wholesale electricity price time series which are characterized by missing data, high volatility, jumps and spikes. To this end, a multi-layer neural network is built and trained based on a dataset containing information on market prices, traded volumes, numbers of trades and counterparties. The forecasts of the trained neural network are used to fill the gaps in the electricity price time series. Starting with the no-gap reconstructed electricity price time series, clustering techniques are then used to identify the largest Gaussian cluster in the log-return empirical distribution. In each market under investigation, we found that log-returns show considerably large Gaussian clusters. This fact allows us to decouple normal stable periods in which log-returns present Gaussian behavior from turbulent periods in which jumps and spikes occur. The decoupling between the stable motion and the turbulent motion enabled us to define suitable mean-reverting jump-diffusion models of power prices and provide an estimation procedure that makes use of the full information contained in both the Gaussian component and the jumpy component of the log-return distribution. The results obtained demonstrate an interesting agreement with empirical data.

我们提出了一种基于深度学习的方法，以调查在电力市场中观察到的复杂的电价动态。目的是：（a）处理具有不规则观察时间的电价时间序列中的缺失数据；（b）检测对数-返回经验分布中的高斯分量（如果存在）；（c）定义电价动态的适当随机模型。我们将此方法应用于美国批发电价时间序列，其特征在于数据缺失，波动性大，波动和尖峰。为此，基于包含关于市场价格，交易量，交易数量和交易对手的信息的数据集，构建和训练多层神经网络。受过训练的神经网络的预测用于填补电价时间序列中的空白。从无间隙重构电价时间序列开始，然后使用聚类技术来确定对数收益率经验分布中最大的高斯聚类。在每个调查的市场中，我们发现对数回报显示出相当大的高斯聚类。这一事实使我们能够将对数返回呈现高斯行为的正常稳定时期与发生跳跃和尖峰的动荡时期脱钩。稳定运动和湍流运动之间的解耦使我们能够定义合适的电价均值回复跳-扩散模型，并提供一个估计程序，该程序利用了日志的高斯分量和跳动分量中包含的全部信息-收益分配。获得的结果表明与经验数据有有趣的一致性。