Electricity markets are considered to be the most volatile amongst commodity markets. The non-storability of electricity and the need for instantaneous balancing of demand and supply can often cause extreme short-lived fluctuations in electricity prices. These fluctuations are termed price spikes. In this paper, we employ a multiclass Support Vector Machine (SVM) model to forecast the occurrence of price spikes in the German intraday electricity market. As price spikes, we define the prices that lie above the 95th quantile estimated by fitting a Generalized Pareto distribution in the innovation distribution of an AR-EGARCH model. The generalization ability of the model is tested in an out-of-the-sample dataset consisting of 4080 hours. Furthermore, we compare the performance of our best SVM model against Neural Networks (NNs) and Gradient Boosted Machines (GBMs)
