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

Understand

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.
• However, a fundamental question arises: how to estimate the performance upper bound of deep time series forecasters? We delve into univariate time series forecasting, a prevalent forecasting paradigm spanning traditional statistical models to advanced time series foundation models.