Comparing Four Time Series Forecasting Methods: Prophet, DeepAR, TFP-STS, and Adaptive AR
Author(s): Shenggang Li Originally published on Towards AI. A practical evaluation of models from Meta, Amazon, Google, and a new adaptive AR approach Time series forecasting is everywhere — in business, finance, retail, and even public policy. The challenge is simple to …
When the Fed Raises Rates: Why Markets Sometimes Cheer and Sometimes Panic
Author(s): Shenggang Li Originally published on Towards AI. Exploring the Nonlinear Dance Between Monetary Policy, Market Narratives, and AI-Powered Learning Models Every time the Federal Reserve announces a rate hike, investors hold their breath. Will stocks plunge because borrowing costs rise and …
Proximal Policy Optimization in Action: Real-Time Pricing with Trust-Region Learning
Author(s): Shenggang Li Originally published on Towards AI. A Practical Guide to Actor–Critic Methods for Dynamic, Data-Driven Decisions Every time a customer opens an app or website, the platform must set a surcharge in milliseconds to balance rider supply, demand spikes, and …
Hybrid Model-Based RL for Intelligent Marketing: Dyna-Q Meets Transformer Models and Bayesian Survival Priors
Author(s): Shenggang Li Originally published on Towards AI. A theory-to-practice study on profit-driven customer re-engagement in e-commerce using BG/NBD-augmented Attention and budget-aware roll-outs We built a next-gen coupon engine fusing three techniques: a Bayesian survival model for repurchase chance, an attention-based Transformer …
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 …
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