## 2 Related Work
combined deep belief networks (DBNs) and MLP [23]. Another important study area in time series prediction is the evaluation of multivariate time series. Likewise, we can observe multivariate time series ubiquitously in everyday life. If there is no correlation between the components of the multivariate time series, then the extra dimensional information does not contribute much to the prediction performance. Thus, in this case, univariate and multivariate predictions produce fairly similar results. Neural networks and deep learning models have also been applied to multivariate time series. For example, in a study conducted by [PERSON] et al., real-world datasets, traffic data and solar energy data were analyzed with multiple CNN models [24]. the traffic dataset had two different sensors, and the solar energy dataset had information from 137 power plant. According to the experiments, multiple CNNs performed better than similar time series model such as the LSTM. In a study conducted by [PERSON] et al. on pre-processing, principal component analysis was used on the original multidimensional input. Then, pre-processed data were used for prediction purposes [25]. [PERSON] et al. proposed a new method by transforming original multivariate data into a higher tensor network [26]. Detailed information about the used algorithms is presented in the Methodology and Experiments section, but to provide preliminary information for the algorithms, some literature knowledge is also presented in this section. The MLP is the basis of deep learning since it consists of a basic unit called the threshold logic unit (TLU). The TLU computes a weighted sum of its inputs and then applies a step function to that sum and outputs the result. MLP architecture consists of three layers: one input layer, some TLU units called hidden layers, and a final output layer of one TLU unit. For a long time, it was believed that the TLU unit is only capable of solving linear problems until the discovery that using a derivable activation function makes it possible to override these limitations [27]. In the literature, MLP models are highly studied, and some modified versions are proposed. For example, a study conducted by [PERSON] investigated how performance is affected while using different activation functions other than step functions [28]. MLP is a supervised model, which means it requires classified data. However, it is possible to use MLP models in a supervised manner by modifying the usage architecture. In a study conducted by [PERSON] et al., to represent the usage of MLP in an unsupervised scenario, input features are mapped to an output cluster node based on the degree of belongingness [29]. The other three models studied in this work belong to RNN and LSTM architecture which are also called \"memory\" models, as they take information from prior inputs to influence the current input and output. These models are designed to process sequential data such as language data or time series data. The core of these kinds of algorithms is the RNN. RNNs can only memorize a limited time frame. Thus, there are many studies on making RNNs more efficient. LSTMs are among one them, and LSTMs are modified versions of RNNs designed to memorize previous information. In another study, RNN was extended to a bidirectional recurrent neural network (BRNN). The study showed that BRNN can be trained without the limitation of user input information up to a preset future frame [30]. Generally, BRNNs and Bi-LSTMs use different configurations and activation functions, but they are both effective in predicting real-life time series data [31; 32]. Another method used in this work is CNN architecture, which is inspired by the convolution operator widely used in electronic engineering. CNNs are very effective, especially on image classification tasks. There are several different configurations of CNNs,such as differential convolutional neural networks, wavenet, etc.