  The scarcity of task-labeled time-series benchmarks in the financial domain
hinders progress in continual learning. Addressing this deficit would foster
innovation in this area. Therefore, we present COB, Crude Oil Benchmark
datasets. COB includes 30 years of asset prices that exhibit significant
distribution shifts and optimally generates corresponding task (i.e., regime)
labels based on these distribution shifts for the three most important crude
oils in the world. Our contributions include creating real-world benchmark
datasets by transforming asset price data into volatility proxies, fitting
models using expectation-maximization (EM), generating contextual task labels
that align with real-world events, and providing these labels as well as the
general algorithm to the public. We show that the inclusion of these task
labels universally improves performance on four continual learning algorithms,
some state-of-the-art, over multiple forecasting horizons. We hope these
benchmarks accelerate research in handling distribution shifts in real-world
data, especially due to the global importance of the assets considered. We've
made the (1) raw price data, (2) task labels generated by our approach, (3) and
code for our algorithm available at https://oilpricebenchmarks.github.io.
