Real World Time Series Benchmark Datasets with Distribution Shifts: Global Crude Oil Price and Volatility

TL;DR

This paper introduces COB, a real-world time series benchmark dataset with distribution shifts based on 30 years of crude oil prices, designed to advance continual learning research in financial data.

Contribution

The authors created and publicly released real-world benchmark datasets with task labels derived from distribution shifts in crude oil prices, facilitating research on handling such shifts.

Findings

Inclusion of task labels improves continual learning performance.

The datasets cover multiple forecasting horizons.

The benchmark datasets are publicly available for research.

Abstract

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.