## 3 Methodology
### Time Series Forecasting Models
#### 3.1.2 Convolutional Neural Networks  
Convolutional Neural Networks (CNN) architectures have been a dominant method in computer vision tasks since the astonishing results were shared on the object recognition competition known as the ImageNet Large Scale Visual Recognition Competition (ILSVRC) in 2012 [27]. Nowadays, they are the state of the art of many classification tasks like object recognition, speech recognition, and natural language processing.  
CNN architecture can extract features from high dimensional raw data with a grid topology without any feature engineering. This can be made with the convolutional method, a sliding filter that creates a features map and captures the repeated patterns at dif  
\begin{table}
\begin{tabular}{c c c c c} \hline \hline  & **Ref** & **Year** & **Technique** & **Application** \\ \hline
**Multi-Agent** & ladjici2014 nash & 2014 & Competitive co-evolutionary algorithms & Calculation of suppliers’ optimal strategies in electricity markets. \\  & rubin2013 impact & 2013 & Nash-Courout framework & Establish the correlations between wind power and electricity prices. \\
**Fundamental** & vevhvilianen2005 stochastic & 2005 & Stochastic factor model & Defining relationships between the fundamental variables and the price formula in the Nordic Market. \\  & carmon2013 electricity & 2013 & Stochastic model & Translation of electricity prices using the demand for the power and the prices of generating fuels. \\
**Reduced-Form** & kaminski1997 challenge & 1997 & [PERSON]’s jump-diffusion model & Modelling electricity prices. \\
**Statistical** & tutnum2016 optimized & 2016 & Elastic Net & Forecasting future net electricity consumption of Turkey. \\  & kuo2018 electricity & 2018 & Conbination fo two nerual networks & Creation of a system for price prediction in the electricity market. \\
**Computational Intelligence** & panapakidis2016 day & 2016 & Artificial Neural Network with clustering & Day-ahead price forecasting. \\  & ugurlu2018 electricity & 2018 & Recurrent Neural Networks & Electricity price forecasting in the Turkish day-ahead market. \\  & chang2019 electricity & 2019 & LSTM Network & Electricity price forecasting using New South Wales dataset of Australia and French. \\  & huang2021 nobel & 2020 & SEPNet & Short-term electricity price prediction using New York City data \\  & sun2021 nobel & 2021 & Deep Neural Network and Autoencoders & Day-ahead electricity price forecasting using Australia data \\ \hline \hline \end{tabular}
\end{table}
Table 1: Related work on electricity market price forecasting within each of the categories (established by [13]) into which the different models published in the literature so far fall.  
ferent input regions. Furthermore, the convolutional method provides to this architecture a characteristic called \"distortion invariance\", which involves that features are extracted no matter where they are in the data.  
The architecture of CNN is composed of convolution layers, pooling layers, and fully connected layers. Furthermore, CNN architecture is based on the principles of:  
1. Local connectivity because each node is connected only to a region of the input.
2. Shared weights because all neurons in the same layers share the same weight matrix for the convolution.
3. Translation equivariance.  
These properties allow CNNs to have a smaller number of trainable parameters than RNNs. For these reasons, CNNs are most efficient in the training time. Another essential aspect of CNNs is the ability to stack different convolutional layers so that the deep learning model can be more high-performance and obtain a better representation of the time series at different scales.