  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. In this paper, we propose Style Miner,
a reinforcement learning method to generate style factors. We first formulate
the problem as a Constrained Markov Decision Process with explanatory power as
the return and stability as the constraint. Then, we design fine-grained
immediate rewards and costs and use a Lagrangian heuristic to balance them
adaptively. Experiments on real-world financial data sets show that Style Miner
outperforms existing learning-based methods by a large margin and achieves a
relatively 10% gain in R-squared explanatory power compared to the
industry-renowned factors proposed by human experts.
