## 1 Introduction
### Techniques for Energy Market Prediction  
Predictions about the energy market, especially energy prices, are highly relevant economically. Unsurprisingly, a large number of different approaches exist. [PERSON] et al. [10] show an attempt to classify modeling approaches according to the state of knowledge at that time, which we extended to capture the methods considered in this comparative study:  
Multi-Agent Approaches  
Model the behavior of different actors on the market by algebraic or differential equations and solve the equation systems to find the market equilibrium, such as the Nash Cournet Framework or Supply function equilibrium. [PERSON], [PERSON], and [PERSON] [24] or [PERSON] et al. [25] are worth mentioning for a sample application of the former and, e.g., [PERSON] et al. [26] of the latter one, respectively. An alternative approach is to simulate the market with the help of agent-based simulation models. This modeling approach is very flexible but requires many assumptions. Here, e.g., [PERSON], [PERSON] and [PERSON] [27] can be referred to for further details.  
Fundamental or structural models  
These models explicitly incorporate fundamental physical and economic relationships in energy production and trading and predict prices with the help of the resulting overall model. These models require detailed information about plant and transmission capacities and demand patterns. They also require assumptions about the physical and economic relationships in the market. See, e.g., [PERSON] and [PERSON] [28], [PERSON] and [PERSON] [29], or [PERSON], [PERSON], and [PERSON] [30] as illustrative examples. Reduced-form models  
This class of models is inspired by financial models of price dynamics, where the intention is usually not to provide a precise hourly forecast. Instead, they aim to capture the characteristics of daily electricity prices, mainly as an input to risk analysis. Jump diffusion models (see [PERSON] and [PERSON] [31]) and Markov regime-switching models (see [PERSON] [32]) can be considered as typical examples.  
Statistical forecast of the current price by a mathematical combination of previous prices and/or previous or current values of exogenous factors. Among others, exponential smoothing (see [PERSON], [PERSON], [PERSON], and [PERSON] [33]), regression models (e.g., [PERSON], [PERSON] and [PERSON] [34]) or AR-type time series models (see [PERSON], [PERSON], [PERSON], and [PERSON] [35]) are typical approaches in that regard.  
Computational intelligence models  
They are supposed to be nature-inspired computational techniques. [PERSON] et al. [10] names here neural networks and support vector machines. See [PERSON], [PERSON], [PERSON], [PERSON], [PERSON], and [PERSON] [36], [PERSON] and [PERSON] [37], [PERSON] et al. [38], or [PERSON] et al. [39] for the usage of neural networks, and [PERSON], [PERSON], and [PERSON] [40], among others, for SVM usage related to Energy Price Forecasting. To reflect the change in the perception of these methods in recent years, we decided to refer to this model type as a machine learning model.  
The focus of this work is price forecasts for the spot market. Therefore, reduced-form methods were not further investigated, focusing on modeling market dynamics and providing input to risk analysis. Multi-agent approaches and fundamental models require much information about the market and assumptions about the physical and economic relationships. Making valid assumptions based on limited information and an increasing number of market actors is a challenging task. Hence, this work focuses on a statistical model and the taxonomy class that [PERSON] et al. [10] referred to as a computational intelligence model. We decided, however, to call them machine learning-based approaches. Figure 2 shows the different models we evaluate in our work in the context of the taxonomy suggested by [10]. The following sections discuss the models in more detail.