Master the mathematical models behind financial prediction. From stationarity and ARIMA through volatility clustering and GARCH to attention-based transformers.
Foundations
Lesson 01
Autocorrelation, stationarity, lag plots, and why temporal structure matters for financial data.
Statistics · MathLesson 02
ADF test, unit roots, differencing, and why models need stationary inputs.
Statistics · MathLesson 03
Autoregressive integrated moving average: model order selection, Box-Jenkins, and one-step forecasts.
ARIMA · PythonVolatility
Lesson 04
Volatility clustering, ARCH effects, and the GARCH(1,1) conditional variance model.
GARCH · FinanceLesson 05
Multi-step variance forecasts, Value at Risk, and asymmetric GARCH variants.
GARCH · FinanceDeep Learning
Lesson 06
Self-attention, positional encoding, and patch-based sequence modelling.
Deep Learning · AttentionLesson 07
Complete forecasting pipeline: ARIMA, GARCH, and Transformer, trained and compared.
Python · Full Build