Optimizing convolutional neural networks for univariate time series forecasting: a comprehensive guide
Mostafa Salaheldin Abdelsalam Abotaleb, Pushan Kumar Dutta · 2024
This chapter delves into the nuanced process of optimizing convolutional neural networks (CNNs) for univariate time series forecasting, an area of critical importance in predictive analytics, spanning various industries, including finance, healthcare, and energy. While CNN models are traditionally celebrated for their prowess in image and spatial data analysis, adapting them for univariate time series data - characterized by sequential observations of a single variable - presents unique challenges and avenues for exploration. The discussion begins with an insightful overview of CNN architectures, underscoring their adaptability in capturing temporal patterns and dependencies through the application of convolutional filters to time series data. Following this, the text investigates the methodologies for tailoring CNNs to the specific demands of univariate forecasting tasks. This includes a thorough examination of data preprocessing techniques, architectural modifications to suit time series analysis, hyperparameter optimization strategies, and the incorporation of regularization techniques to enhance model precision while preventing overfitting. A significant contribution of this research is the formulation of an exhaustive framework for deploying and critically evaluating CNN models, bolstered by empirical analysis on real-world datasets to verify the proposed adjustments’ efficacy. Comparative performance evaluations reveal the superiority of meticulously optimized CNN models against traditional forecasting methods and generic neural network architectures. This article is intended to serve as a definitive guide for both practitioners and researchers seeking to leverage the sophisticated predictive power of CNNs for univariate time series forecasting, providing detailed instructions for the careful development, assessment, and implementation of these models.