Adaptive Forecasting

Adaptive forecasting focuses on developing models that can accurately predict future values in time series data despite changes in underlying patterns or distributions over time. Current research emphasizes methods that dynamically adjust to these non-stationary characteristics, employing techniques like ensemble models with recency weighting, Markov switching models, and novel neural network architectures such as Kolmogorov-Arnold Networks and spiking neural networks. These advancements improve forecasting accuracy and robustness in diverse applications, ranging from process control in industrial settings to predicting public health crises and optimizing resource allocation in transportation systems.