Stock Market Prediction using HMMs
Time‑series modeling with HMMs to infer market regimes and forecast behavior — a probabilistic approach to noisy financial data.
Overview
A probabilistic modeling project that uses Hidden Markov Models to capture latent market regimes and make regime‑aware forecasts on stock price movements.
Problem
Financial time series are noisy and non‑stationary. Simple models struggle when the underlying regime shifts — bull, bear, volatile, calm.
Approach
Preprocessed time‑series data into observation features suitable for HMM training. Trained models to infer hidden states (regimes) and used those states to drive predictions. Validated results with careful evaluation and emphasized interpretability of learned regimes.
Impact
A strong “math‑meets‑engineering” project: showcases probabilistic reasoning, time‑series intuition, and the ability to explain why a model behaves the way it does.