Online Time Series

Online time series forecasting focuses on building models that can adapt to continuously arriving data, addressing the challenge of concept drift and non-stationarity in real-world time series. Current research emphasizes developing models that efficiently combine multiple expert predictors, often using ensemble methods, stochastic filtering, or reinforcement learning to dynamically adjust model weights and parameters. These advancements aim to improve forecasting accuracy and adaptability in applications ranging from financial markets to renewable energy prediction, where rapid response to changing patterns is crucial. The field is actively exploring novel architectures like hyperdimensional computing and spiking neural networks to enhance efficiency and reduce computational demands, particularly for edge computing scenarios.