Exponential Smoothing

Exponential smoothing (ES) is a family of time series forecasting methods aiming to predict future values by weighting past observations exponentially, giving more importance to recent data. Current research focuses on enhancing ES's capabilities through hybrid models combining it with neural networks (like RNNs and LSTMs), Bayesian approaches for improved uncertainty quantification, and novel algorithms for efficient model selection and parameter estimation. These advancements improve forecasting accuracy and efficiency across diverse applications, including financial time series analysis, air quality prediction, and contextual bandit problems, demonstrating ES's continued relevance in a rapidly evolving field.