## 2 Related Work  
Forecasting the future behavior of a system can be solved by predicting time series data. Thus, predicting time series is essential and has been studied widely in various domains such as finance, climate, logistics, crime, medicine, etc. [9]. It is possible to divide the works into two main categories. The first group of works uses methods from classical machine learning algorithms based on statistical techniques. The second group of works uses deep learning models. Even though this work mainly focuses on deep learning models, it is important to emphasize traditional machine learning studies on time series forecasting since models such as support vector machines (SVM), decision trees, and other methods have been widely used before the deep learning revolution in 2012. For example, in 2003, [PERSON] employed an SVM model on financial time series data [10], and in 2009, [PERSON] et al. used a fuzzy decision tree model for predicting financial data [11]. In 2004, [PERSON], and [PERSON] used rough sets from S&P 500 data [12]. In 2015, [PERSON] et al. conducted a two-phased experiment using support vector regression and random forest [13]. Another study was conducted by [PERSON] et al. They adopted the SVM model and multi-layer perceptron (MLP) for predicting stock price movements in China's market [14]. In another study, the classification performance of some of the classical machine learning algorithms was evaluated on the same data, and the results are presented in [15]. Classical machine learning models are generally based on statistical theories and are used to solve the regression problem, which is the main task in time series prediction. Even though these models are used as time series predictors, they lack memorizing previous data that fail to predict and adapt to sharp changes in trends. On the other hand, deep learning models such as LSTMs are capable of memorizing previous data and thus producing more agile predictions [16]. Even on stock data, which event-driven, lowly correlated, and unstable data, LSTM models perform well. For example, in a study conducted by [PERSON] et al., an LSTM-based approach resulted in a 13% improvement in accuracy over classical machine learning models [17]. Another study with the LSTM model was conducted on the same dataset by [PERSON] and yielded satisfactory results over five-day-long intervals [18]. In this work, a relatively bigger window was used for utilizing the memorizing power of LSTM architecture. [PERSON] et al. followed a similar approach as we did in this study by comparing the prediction performances of LSTM, RNN, and CNN models of three layered networks [19]. They tested deep learning models on different time series data from public datasets such as S&P 500 Daily Closing Prices stock data, Nikkei 225 Daily Closing Prices stock data, etc. According to their findings, an LSTM-based model that consisted of one bidirectional LSTM layer, followed by a dropout layer and fully connected layer, outperformed other models. Financial time series forecasting studies constitute the majority of the research. Studies have been conducted on various data such as stock prices, bond prices, forex prices, cryptocurrency prices, etc. Most of the studies use price data as the only input, while some studies use price data and financial news data together [8]. These studies use hybrid deep learning models by combining memory cells, such as LSTM, and natural language processing (NLP) methods, such as BERT, word2 vec embedding, etc. [20; 21]. LSTM and variations of LSTM models dominate the financial time series forecasting studies. These text-mining hybrid works are aimed at solving financial time series problems that naturally consist of event-driven patterns. Thus, adding some textural information from financial news by using NLP techniques helps to increase the accuracy of predictions. To increase the accuracy in financial time series prediction is to using hybrid models in a single network. For example, [PERSON] et al. applied CNN and LSTM [22], and [PERSON] et al.