Introduction

Why Time Series Analysis?

Financial prices are time series — sequences of values indexed by time. Understanding their structure is essential for forecasting, risk management, and strategy design. This course implements the canonical models of time series analysis in pure Python, from basic autocorrelation to GARCH volatility modeling.

You will implement:

  • Autocorrelation — ACF, PACF, stationarity tests, differencing, and integration
  • ARMA Models — MA(q), AR(p), ARMA(p,q), and ARIMA forecasting
  • Smoothing Methods — Exponential smoothing and Holt-Winters linear trend
  • Volatility Models — Volatility clustering, ARCH, GARCH(1,1), realized volatility, and Kalman filtering
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