A Newton-Raphson-Based Optimizer-Driven Temporal Convolutional Networks for Birth Rate Prediction in a Small Area

For economically developed small geographic regions, population forecasting serves as a vital tool for achieving refined regional management. However, due to relying on the subjective experience of experts, traditional methods for predicting birth rates have shortcomings in accuracy, resulting in unreliable results. To address this limitation, this study introduces deep learning (DL) models into the domain of birth rate prediction. Specifically, a hybrid TCN-Bi-LSTM model is proposed, integrating a Temporal Convolutional Network (TCN) with a Bi-directional Long Short-Term Memory (Bi-LSTM) network to predict birth populations in small regions. The proposed hybrid model effectively leverages the strengths of the TCN and Bi-LSTM to capture both local temporal patterns and long-term hidden dependencies within birth rate time series data. The proposed birth rate prediction model not only incorporates historical data on regional birth rates but also accounts for the influence of factors such as divorce rates, consumption levels, and population size. Furthermore, an enhanced meta-heuristic algorithm is designed to optimize the hyperparameters of the hybrid TCN-Bi-LSTM model, with the aim of increasing its prediction accuracy. The hippopotamus position update strategy was introduced into the Newton-Raphson-Based Optimizer (NRBO), and an improved NRBO (INRBO) algorithm was developed. Finally, the performance of the proposed birth rate prediction model was validated using a dataset from three regions or countries. The prediction results demonstrate that, compared to the other four models, the proposed INRBO-TCN–Bi-LSTM model achieves the best performance, with an average reduction of 95% in training loss.