Time Series Analysis aims to understand the behavior of a time series, identify patterns and trends, forecast future values, and determine the underlying structure that generates the data.

Cross-correlation measures the linear relationship between two time series, indicating how one series is correlated with the other over different time lags.

Stationarity refers to the property of a time series where its mean, variance, and autocorrelation structure remain constant over time.

Differencing is a technique used to remove trend and seasonality from a time series by subtracting the previous value from the current value.

The order of an ARMA model is denoted as (p, q), where p is the number of autoregressive terms and q is the number of moving average terms.

ARIMA models are commonly used for forecasting time series data, as they capture the autoregressive and moving average components of the series.

The AIC is a statistical measure used to compare different models and select the one that best fits the data while penalizing for overfitting.

Grubbs' test is a statistical test used to identify outliers in a time series by comparing each data point to the mean and standard deviation of the series.

Seasonal differencing is a technique used to remove seasonality from a time series by subtracting the value at the same time lag from the previous season.

The autocorrelation function (ACF), partial autocorrelation function (PACF), and cross-correlation function (CCF) are all commonly used to visualize the autocorrelation structure of a time series.

The Ljung-Box test is a statistical test used to test for autocorrelation in a time series by comparing the sample autocorrelation coefficients to the expected values under the assumption of no autocorrelation.

An autoregressive (AR) model is a linear regression model that uses past values of a time series to forecast future values.

The Dickey-Fuller test is a statistical test used to test for stationarity in a time series by comparing the estimated autoregressive coefficient to the critical values.

Exponential smoothing, moving average, and differencing are all commonly used methods for smoothing a time series to remove noise and reveal underlying trends.

The Box-Jenkins approach is a systematic procedure for identifying and fitting an appropriate ARMA model to a time series.