Frequency Domain Representation

Frequency domain representation transforms data from a time-based perspective to a frequency-based one, revealing underlying periodicities and patterns often hidden in raw time series or other sequential data. Current research focuses on integrating frequency domain representations with various model architectures, including convolutional neural networks (CNNs), transformers, and Gaussian Mixture Models (GMMs), to improve performance in tasks like time series forecasting, anomaly detection, and image processing. This approach offers advantages in computational efficiency and the ability to capture both local and global dependencies within data, leading to improved accuracy and reduced computational cost in diverse applications. The resulting improvements in model performance and efficiency are driving significant advancements across numerous fields.