![]() TLS-LSTM algorithm is presented to improve the accuracy (h) was used to determine the predictability of the Australian Dollar and United Statesĭollar (AUD/USD) dataset. Network to forecast the trend and conduct a correlation analysis. The goal of this study is to choose a dataset using the HurstĮxponent, then use a two-layer stacked Long Short-Term Memory (TLS-LSTM) neural Learning capacity, the LSTM neural network is increasingly being utilized to predictĪdvanced Forex trading based on previous data. Term Memory (LSTM) neural network, which is a special kind of artificial neural networkĭeveloped exclusively for time series data analysis, is frequently used. As a result, a slew of research articles aimedĪt improving the accuracy of currency forecasting has been released. Several areas, including the Forex market. In recent decades has allowed artificial neural networks to be effectively adapted to It is always a question of how precise a Forex prediction can be because Accurately anticipating theįorex trend has remained a popular but difficult issue to aid Forex traders’ tradingĭecisions. ![]() (Forex) market has attracted a large number of investors. Since it is one of the world’s most significant financial markets, the foreign exchange In addition, this study explores the characteristics of high noise and complexity of corporate stock time series, designs a DNN prediction model, and verifies the feasibility of the LSTM model to predict corporate stock changes with high accuracy. The prediction accuracy is better than other prediction models. Through the forecast trend analysis under different models, LSTM predicts that the stock change trend of the enterprise model is closest to the changing trend of the actual earnings price. This figure is closest to the average real return price of 13.89. The average return price of the LSTM prediction model is 14.01. The results show that as the number of iterations increases, the loss rate of the LSTM training curve keeps decreasing until 0. The 20-day change trend of the company’s stock returns under different models is predicted and analyzed. LSTM prediction models are used to perform error analysis on company data training. The uniqueness and innovation lie in using the stock returns of Bank of China securities in 2022 as the training data set. A prediction trend model of enterprise stock is established based on long short-term memory (LSTM). Secondly, the inadequacies of deep neural network (DNN) models are discussed. Firstly, the relevant theories of stock forecasting are discussed, and problems in stock forecasting are raised. In building a financial forecasting model, historical data and learned parameters are used to predict future stock prices. This study aims to accurately predict the changing trend of stocks in stock trading so that company investors can obtain higher returns. Our proposed hybrid model, which combines two separate LSTMs corresponding to these two data sets, was found to be quite successful in experiments using real data. We utilized two different data sets-namely, macroeconomic data and technical indicator data-since in the financial world, fundamental and technical analysis are two main techniques, and they use those two data sets, respectively. In this work, we used a popular deep learning tool called “long short-term memory” (LSTM), which has been shown to be very effective in many time-series forecasting problems, to make direction predictions in Forex. The direction prediction requirement makes the problem quite different from other typical time-series forecasting problems. However, incorrect predictions in Forex may cause much higher losses than in other typical financial markets. It is also a very simple market since traders can profit by just predicting the direction of the exchange rate between two currencies. Forex (foreign exchange) is a special financial market that entails both high risks and high profit opportunities for traders.
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