time-series-analysis

From the author

Analyzing and forecasting data ordered in time. Covers stationarity and differencing, autocorrelation (ACF and PACF), AR, MA, ARMA, and ARIMA models, the Box-Jenkins methodology, seasonality and SARIMA, forecasting with prediction intervals, and residual diagnostics including the Ljung-Box test. Use when data is indexed by time, when forecasting future values, when detecting trend or seasonality, or when checking whether residuals still carry structure.