Research on Financial Time Series Prediction based on Neural Network

At present, financial time series forecasting analyzes and predicts future movements in financial markets through the use of various technical means. However, financial time series data often exhibit significant nonlinear characteristics, which brings many challenges to forecasting future development. The model initiates the preprocessing of the input financial time series data with the objective of removing noise and standardising the data. Subsequently, the model employs a hierarchical long-short-term memory (LSTM) neural network structure, wherein the long-short-term units of each layer are designed to capture dependencies at varying time scales. The underlying LSM unit is primarily responsible for capturing short-term dependencies, while the upper LSM unit is tasked with extracting long-term trends and global patterns. Furthermore, the model incorporates an attention mechanism that enables it to adaptively modulate the level of attention at distinct time points, thereby facilitating more precise identification and accentuation of the temporal instances that are most pivotal to the prediction outcome. To enhance the precision of the model's predictions, an adaptive learning rate optimiser is employed, enabling the model to autonomously calibrate the training procedure in accordance with the data's complexity. In the experimental analysis, the proposed model is compared with the benchmark model, and the results demonstrate that it is more effective in capturing the intricate patterns of financial time series data.