Singular Spectrum Analysis

Singular Spectrum Analysis (SSA) is a powerful time series analysis technique used to decompose data into trend, seasonal, and noise components, enabling improved forecasting and noise reduction. Current research focuses on integrating SSA with other advanced models, such as recurrent neural networks (RNNs) and temporal convolutional networks (TCNs), to enhance prediction accuracy in diverse applications like wind speed forecasting and sequential recommendation. These hybrid approaches leverage SSA's ability to pre-process noisy data, improving the performance of more complex models and leading to more robust and accurate results across various fields. The impact of this research extends to improved forecasting in renewable energy management, enhanced recommendation systems, and more effective anomaly detection in time-series data.