Research on Time-Series-Data-Prediction Based on Neural Network Technology

This paper focuses on the application of advanced neural network frameworks, specifically Long Short-Term Memory (LSTM) networks, to tackle complex time series data prediction challenges. It aims to elucidate the methodology, model architecture, parameter optimization, and potential applications of LSTM-based models in the context of time series forecasting. Additionally, the study incorporates supplementary techniques such as dynamic windows to enhance prediction accuracy. LSTM networks are employed as a central component of this research due to their recurrent nature and memory retention capabilities, which make them well-suited to capturing temporal dependencies and patterns inherent in time series data. The model architecture is thoughtfully designed, considering factors such as the number of LSTM layers, hidden units, and dropout rates, to align with the specific characteristics of the dataset. Parameter tuning is performed through an extensive iterative process, encompassing over 200 training and validation iterations, to maximize model performance. To illustrate the proposed methodology's practicality, the study includes a real-world example involving “Wordle” lexical puzzles. This application serves as empirical evidence of the effectiveness and applicability of LSTM-based models in solving complex time series prediction problems. Looking forward, LSTM-based models hold significant promise in various domains, including finance, weather forecasting, and healthcare, among others. As computational resources continue to advance, the ability to train more intricate LSTM architectures offers the potential to enhance predictive accuracy further.