Latent Inference-Time Guidance of Time Series Foundation Models
Smart blending of AI forecasts by learning what each does best
Time series foundation models—AI systems that predict future values in data streams—produce forecasts that vary in quality depending on how you set them up, but no single configuration works best for all situations. Researchers developed a method that automatically combines multiple forecasts from the same model by learning in latent space which configurations work well for different parts of the time series, achieving results competitive with traditional ensemble methods while keeping the models off-the-shelf.
Organizations using AI for forecasting—from energy demand prediction to stock prices—currently either gamble on a single configuration or manually try many combinations. This approach removes that guesswork by automatically learning which settings work best for different parts of your data, improving forecast accuracy without requiring custom retraining or proprietary tools.