![]() Their system reported the predicted values of the company stock price in the Indian stock market for 30 days. used LSTM for swing traders to predict future stock values. For each cryptocurrency, they used different LSTM models and concluded that the model that is based on 50 days of data performed the best since it has the capability to capture long-term dependencies. tested forecasting models using daily cryptocurrency prices. Moreover, they found that the classification model was more effective than regression models for algorithmic trading. They showed that DNN-based models were the best for price trend prediction of Bitcoin. compared deep learning methods such as a deep neural network (DNN), a convolutional NN, and a deep residual network, for Bitcoin price prediction. Results showed an average of 55.9% accuracy in predicting stock price trend shifts. used LSTM networks with technical analysis indicators to predict future stock prices. They concluded that the model performed better than benchmark models with lasso and ridge logistic classifiers or equally weighted ensembles. ensembled an LSTM model that uses many technical indicators as the network inputs for predicting intraday stock movements of U.S. This can be very useful for the financial assets trading system by setting a specific range of data that the designer wants the algorithm to perform predictions while at the same time, deciding upon the range and scope of data that the system should forget in order not to be destructed by irrelevant data. The major difference between LSTMs over traditional ANN is that they can learn selectively by remembering and forgetting the required historical data. They showed that the Radial Basis Gaussian (RBG) SVM forecasting model outperformed other SVM-kernel models. used SVM kernel functions to predict the price directions of cryptocurrency and foreign exchange. examined the predictability of the 12 cryptocurrency prices using SVM and concluded at the daily or minute level frequencies, ML classification algorithms reach about 55–65% of predictive accuracy. used the SVM algorithm to make automated transaction decisions on the Forex market and proved the system’s contribution to successful trading. Furthermore, they offered an improved model that combines SVM with the other classification methods. ![]() tested the predictability of the NIKKEI 225 index weekly movement direction with the SVM forecasting model and proved that SVM outperforms other classification methods. They concluded that their model is more suitable for stock price forecasting than the single forecasting model. combined a cumulative auto-regressive moving average with a least squares SVM model to make basic predictions for the stock market. The process allows error within the training data resulting in a significantly reduced error in the tested data. Support vector machine (SVM) is a supervised learning method aimed to predict and classify items using linear or nonlinear processes. ![]()
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