Decoding Optimization Algorithms for Convolutional Neural Networks in Time Series Regression Tasks

Optimization algorithms play a vital role in training deep learning models effectively. This research paper presents a comprehensive comparative analysis of various optimization algorithms for Convolutional Neural Networks (CNNs) in the context of time series regression. The study focuses on the specific application of maximum temperature prediction, utilizing a dataset of historical temperature records. The primary objective is to investigate the performance of different optimizers and evaluate their impact on the accuracy and convergence properties of the CNN model. Experiments were conducted using different optimizers, including Stochastic Gradient Descent (SGD), RMSprop, Adagrad, Adadelta, Adam, and Adamax, while keeping other factors constant. Their performance was evaluated and compared based on metrics such as mean squared error (MSE), mean absolute error (MAE), root mean squared error (RMSE), R-squared (R²), mean absolute percentage error (MAPE), and explained variance score (EVS) to measure the predictive accuracy and generalization capability of the models. Additionally, learning curves are analyzed to observe the convergence behavior of each optimizer. The experimental results, indicating significant variations in convergence speed, accuracy, and robustness among the optimizers, underscore the research value of this work. By comprehensively evaluating and comparing various optimization algorithms, we aimed to provide valuable insights into their performance characteristics in the context of time series regression using CNN models. This work contributes to the understanding of optimizer selection and its impact on model performance, assisting researchers and practitioners in choosing the most suitable optimization algorithm for time series regression tasks.