Algorithm acceleration technology of machine learning in financial time series forecasting
Hong Xia Pan · International Scientific Technical and Economic Research · 2025
As the complexity of financial markets continues to increase, traditional financial time series forecasting methods face huge challenges. Machine learning, especially deep learning, has become a powerful tool to meet this challenge. However, these models often require a lot of computing resources when processing large-scale data, resulting in delays in the training and prediction process. In order to improve the efficiency and real-time performance of prediction, machine learning algorithm acceleration technology has emerged, mainly through hardware acceleration and software optimization to improve the speed of model training and reasoning. This study explores the application of machine learning algorithm acceleration technology in financial time series forecasting, analyzes how acceleration technology can help solve problems in data processing, real-time forecasting, and other aspects, and demonstrates the actual effects of these technologies in the stock market, foreign exchange market, and commodity market through case analysis. Despite the challenges of data diversity and hardware resource limitations, future research directions will focus on the combination of deep reinforcement learning, cloud computing, edge computing, and quantum computing to further promote the intelligent and efficient development of financial time series forecasting.