Time Series Analysis

Time series analysis focuses on extracting meaningful patterns and insights from data collected over time, primarily aiming to forecast future values, detect anomalies, or classify temporal sequences. Current research emphasizes the development of sophisticated models, including neural networks (like CNN-LSTMs, Transformers, and MLP-Mixers), wavelet-based approaches, and hybrid methods combining statistical techniques with deep learning, to handle increasingly complex and high-dimensional datasets. These advancements have significant implications across diverse fields, improving accuracy in applications ranging from weather prediction and financial forecasting to healthcare monitoring and industrial process optimization.