Temporal Graph Neural Networks for Multi-Product Time Series Forecasting
Author(s): Shenggang Li Originally published on Towards AI. Modeling Cross-Series Dependencies and Temporal Dynamics in Retail Supply-Chain Data This paper uses a supply-chain scenario — forecasting daily sales for multiple products — to explore Graph Neural Networks (GNNs) and their temporal extensions …
Using Reinforcement Learning to Solve Business Problems
Author(s): Shenggang Li Originally published on Towards AI. Exploring RL Concepts and Applications Through a Customer Engagement Example As a data scientist in any industry, if you’ve spent your career building supervised learning models — predicting customer churn, segmenting users, or forecasting …
Bank Wealth Planning — Dynamic AI “Broker Guider” Platform
Author(s): Shenggang Li Originally published on Towards AI. Real-time, constraint-aware portfolio rebalancing for advisors and clients This platform adjusts portfolio weights to each investor’s goals while honoring risk, liquidity, turnover, and tax rules. It ingests client profiles and live market data, then …
Beyond Associations: Reinforcement Learning for Sequential Market Basket Decisions
Author(s): Shenggang Li Originally published on Towards AI. Clustered contextual bandits and tabular Q-learning with off-policy evaluation on real-world retail logs Traditional market basket analysis (MBA) explains what tends to co-occur, but it does not decide what to do next. This paper …
Beyond Associations: Reinforcement Learning for Sequential Market Basket Decisions
Author(s): Shenggang Li Originally published on Towards AI. Clustered contextual bandits and tabular Q-learning with off-policy evaluation on real-world retail logs Traditional market basket analysis (MBA) explains what tends to co-occur, but it does not decide what to do next. This paper …
Bank Wealth Planning — Dynamic AI “Broker Guider” Platform
Author(s): Shenggang Li Originally published on Towards AI. Real-time, constraint-aware portfolio rebalancing for advisors and clients This platform adjusts portfolio weights to each investor’s goals while honoring risk, liquidity, turnover, and tax rules. It ingests client profiles and live market data, then …
Reinforcement Learning for Next-Gen AML: From Rules to Dynamic Decisioning
Author(s): Shenggang Li Originally published on Towards AI. A Practical Guide Combining Causal RL, and Thompson-Sampling for Watch-List Prioritization and Peer-Group Outlier Detection Anti-Money Laundering (AML) operations are facing a dual challenge: growing regulatory pressure and increasing transaction complexity. Traditional rule-based systems …
Preference Learning and Deep Reinforcement Learning (TD3) for Multi‑Manager Portfolio Strategy Selection
Author(s): Shenggang Li Originally published on Towards AI. From Human Manager Trajectories to a Unified Adaptive Allocation Policy via AHP‑Guided Preference Modeling and Actor–Critic Optimization Traditional asset allocation faces challenges in balancing multiple objectives: Photo by Markus Spiske on UnsplashThis article addresses …
Plug-and-Play Reinforcement Learning for Real-Time Forecast Recalibration
Author(s): Shenggang Li Originally published on Towards AI. Updating legacy ARMA sales models with a PPO residual corrector — no full retrain requiredPhoto by Anders Jildén on Unsplash When I build a time-series model — say an ARMA trained on last season’s …
Distill-then-Detect: A Practical Framework for Error-Aware Machine Learning
Author(s): Shenggang Li Originally published on Towards AI. Leveraging Teacher Uncertainty, Student Distillation, and Conformal Calibration to Diagnose and Flag High-Risk PredictionsPhoto by Sigmund on Unsplash Even the most advanced neural networks or boosting algorithms sometimes stumble on a small but critical …
From Static to Dynamic: Evolving Bayesian Network Thinking for Real-World Applications
Author(s): Shenggang Li Originally published on Towards AI. Applied Bayesian Networks: Bridging Theory, Modeling, and Forecasting in PracticePhoto by Abi Ghouta Timur on Unsplash Imagine you’re a supply-chain manager trying to predict equipment failures before production halts. Begin by mapping key factors …
Hybrid Attention for Binary Sequence Forecasting
Author(s): Shenggang Li Originally published on Towards AI. Combining n-Gram Embeddings, Count-Aware Self-Attention, and Recency-Weighted ARMA for Multi-Horizon Distributional PredictionsPhoto by Pasqualino Capobianco on Unsplash I tackle pure binary time series forecasting by converting complex signals into 0/1 patterns — stock up/down, …
Designing Customized and Dynamic Prompts for Large Language Models
Author(s): Shenggang Li Originally published on Towards AI. A Practical Comparison of Context-Building, Templating, and Orchestration Techniques Across Modern LLM FrameworksPhoto by Free Nomad on Unsplash Imagine you’re at a coffee shop, and ask for a coffee. Simple, right? But if you …
A Novel and Practical Meta‑Booster for Supervised Learning
Author(s): Shenggang Li Originally published on Towards AI. A Stacking‑Enhanced Margin‑Space Framework for Dynamic, Loss‑Driven Ensemble Updates in Classification and RegressionPhoto by Thorium on Unsplash Ensemble methods thrive on diversity, yet most frameworks exploit it sequentially (boosting) or statically (stacking). We introduce …
Adaptive Decay-Weighted ARMA: A Novel Approach to Time Series Forecasting
Author(s): Shenggang Li Originally published on Towards AI. Integrating Recency-Based Loss Weighting and Seasonal Feature Tuning for Enhanced Predictive AccuracyPhoto by Haberdoedas II on Unsplash Time series forecasting is both fascinating and challenging. It’s fascinating because accurate predictions can directly inform better …
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