The Evolution of Time Series Analysis: Beyond Traditional Forecasting

Abstract

The field of time series forecasting has undergone a profound transformation, evolving from traditional statistical foundations to sophisticated deep learning innovations. Modern neural networks and machine learning models now offer enhanced capabilities for capturing complex patterns and non-linear relationships, often surpassing conventional approaches. Key architectural advancements, such as attention mechanisms and transformer architectures, have revolutionized the processing of sequential data. Concurrently, the emergence of automated machine learning (AutoML) and explainable AI (XAI) has significantly streamlined model development and improved interpretability. These developments hold particular significance for domains requiring multi-dimensional analysis and real-time predictions, where advanced architectures excel at discerning intricate relationships between variables while maintaining computational efficiency.