Time series forecasting is one of the most important procedures within various real-world machine learning applications. However, the complexity and non-stationary nature of many realistic time series data challenges people to make reliable predictions. In this paper, we propose a novel Causal Stacking Hidden Markov model termed CASH to make reliable predictions while mining effective hidden states. CASH leverages structural invariances of causal inference and efficient hidden state uncovering capability of the Hidden Markov Model (HMM). It attributes time series signal movements to short-term, medium-term, and long-term factors, taking into account all three by stacking HMMs in-depth and enhanced by an attention mechanism. Subsequently, utilizing a reformulated sequential variational auto-encoder objective function, CASH learns effective hidden states in an unsupervised manner. For the final prediction, the hidden states are processed in a temporal attention layer to leverage labels. Comprehensive experiments on three realistic financial datasets and twenty multivariate time series classification datasets demonstrate the proposed CASH outperforms the state-of-the-art benchmarks on most datasets.

Portfolio management is a fundamental problem in finance. It involves periodic reallocations of assets to maximize the expected returns within an appropriate level of risk exposure. Deep reinforcement learning (RL) has been considered a promising approach to solving this problem owing to its strong capability in sequential decision making. However, due to the non-stationary nature of financial markets, applying RL techniques to portfolio optimization remains a challenging problem. Extracting trading knowledge from various expert strategies could be helpful for agents to accommodate the changing markets. In this paper, we propose MetaTrader, a novel two-stage RL-based approach for portfolio management, which learns to integrate diverse trading policies to adapt to various market conditions. In the first stage, MetaTrader incorporates an imitation learning objective into the reinforcement learning framework. Through imitating different expert demonstrations, MetaTrader acquires a set of trading policies with great diversity. In the second stage, MetaTrader learns a meta-policy to recognize the market conditions and decide on the most proper learned policy to follow. We evaluate the proposed approach on three real-world index datasets and compare it to state-of-the-art baselines. The empirical results demonstrate that MetaTrader significantly outperforms those baselines in balancing profits and risks. Furthermore, thorough ablation studies validate the effectiveness of the components in the proposed approach.