time series analysis

**Time Series Analysis** in semiconductor manufacturing is the **study of sequential process data ordered by time** — analyzing trends, seasonality, autocorrelation, and change points to understand process dynamics, predict future behavior, and detect shifts. **Key Time Series Methods** - **Trend Analysis**: Moving averages, exponential smoothing, and regression for identifying long-term drift. - **ARIMA**: Auto-Regressive Integrated Moving Average models for forecasting and anomaly detection. - **Change Point Detection**: CUSUM, PELT algorithms detect when the process mean or variance shifts. - **Spectral Analysis**: FFT reveals periodic patterns (shift effects, PM cycles, seasonal variations). **Why It Matters** - **Drift Detection**: Identifies gradual process drift before it exceeds specification limits. - **PM Scheduling**: Correlates time patterns with preventive maintenance cycles. - **Forecasting**: Predicts future process state to enable proactive corrections. **Time Series Analysis** is **reading the process heartbeat** — understanding how process parameters evolve over time to detect, predict, and prevent excursions.