The field of time series forecasting and dynamic portfolio optimization is rapidly advancing, with a focus on developing innovative frameworks and models that can effectively capture temporal dependencies and adapt to non-stationary market regimes. Recent research has highlighted the importance of addressing challenges such as data unalignment, imbalanced short-input long-output sequences, and volatility in real-world data. The use of deep learning models, such as hybrid LSTM and PPO networks, has shown promise in delivering higher returns and stronger resilience in dynamic portfolio optimization. Furthermore, the development of novel frameworks like Trapezoidal Temporal Fusion has improved forecasting accuracy in user growth scenarios. Noteworthy papers include:

• TTF: A Trapezoidal Temporal Fusion Framework for LTV Forecasting in Douyin, which proposes a novel framework for addressing challenges in LTV forecasting.
• Hybrid LSTM and PPO Networks for Dynamic Portfolio Optimization, which introduces a hybrid framework for portfolio optimization that leverages the predictive power of deep recurrent networks and reinforcement learning.
• Optimization of Deep Learning Models for Dynamic Market Behavior Prediction, which presents a hybrid sequence model that combines multi-scale temporal convolutions, a gated recurrent module, and time-aware self-attention for multi-horizon demand forecasting.