High-Frequency Data Gap Exposes TSFM Limitations
Researchers have released a new millisecond-resolution dataset capturing real-world 5G wireless and traffic conditions to address a critical gap in time series foundation model training. Current large-scale datasets focus on low-frequency data sampled at intervals of seconds to years, limiting TSFMs' ability to handle high-frequency phenomena. This dataset introduces wireless networks as a new domain for TSFM pre-training and includes forecasting tasks with prediction horizons from 1 to 96 milliseconds. Benchmarking reveals that most TSFM configurations perform poorly on this high-frequency data in both zero-shot and fine-tuned settings, highlighting the need for high-frequency datasets during pre-training to improve generalization and robustness.