Exploring Accuracy Law for Deep Time Series Forecasters: An Empirical Study

ArXi:2510.02729v2 Announce Type: replace Deep time series forecasting has emerged as a rapidly growing field in recent years. Despite the exponential growth of community interests, progress on standard benchmarks is often limited to marginal improvements. A common consensus of the community is that time series forecasting inherently faces a non-zero error lower bound due to its partially observable and uncertain nature.