Style Miner: Find Significant and Stable Explanatory Factors in Time Series with Constrained Reinforcement Learning

Understand

In high-dimensional time-series analysis, it is essential to have a set of key factors (namely, the style factors) that explain the change of the observed variable.

• For example, volatility modeling in finance relies on a set of risk factors, and climate change studies in climatology rely on a set of causal factors.
• The ideal low-dimensional style factors should balance significance (with high explanatory power) and stability (consistent, no significant fluctuations).
• However, previous supervised and unsupervised feature extraction methods can hardly address the tradeoff.