When it comes to the interpretation and prediction of temporal data, deep learning algorithms have made significant strides. In recent years, the rapid rise of financial technology and artificial intelligence has led to their widespread use across a broad range of sectors, including methods such as machine learning and deep learning, among others. The financial markets have a magical air that attracts a varied spectrum of investors to take part in the game of chance.
In addition, our LSTM-P model outperforms both the conventional LSTM models and other time series forecasting models in terms of accuracy and precision. As a consequence of our model, we have a high degree of accuracy when projecting future pricing. Third, in the price prediction model, we develop an optimized LSTM prediction model (LSPM-P) and train it using historical price data for gold and Bitcoin to make accurate predictions. Second, we apply a wavelet transform to diminish the influence of high-frequency noise components on prices.
We first employ a noise reduction approach based on the wavelet transform to smooth the fluctuations of the price data, which has been shown to increase the accuracy of subsequent predictions. Using the historical price series of Bitcoin and gold from to, we investigate an LSTM-P neural network model for predicting the values of Bitcoin and gold in this research.
As a result of the fast growth of financial technology and artificial intelligence around the world, quantitative algorithms are now being employed in many classic futures and stock trading, as well as hot digital currency trades, among other applications today.