# ARMA/ARIMA Analysis

Analyze your time series, exploer hidden serial correlations and/or seasonality. Model your data with sophisticated econometric processes, and forecast future outcomes.

## ARMA/ARIMA Analysis with NumXL

In general ARMA/ARIMA process assumes that future values of $X_t$ are linearly dependent on its past values (i.e. $X_{t-1},X_{t-2},\cdots$) and the error terms $\epsilon_t$, unless specified otherwise.

### ARMA Modeling

Analyze your time series, explore hidden serial correlations. Model your data with sophisticated econometric processes, and forecast future outcomes

### ARIMA Modeling

Analyze your non-stationary time series, explore hidden serial correlations, model with sophisticated econometric processes, and forecast future outcomes.

### SARIMA Modeling

Analyze your time series, exploer hidden serial correlations and/or seasonality. Model your data with sophisticated econometric processes, and forecast future outcomes

### SARIMAX Modeling

Analyze your time series in presence of exogenous factors, explore hidden serial correlations and/or seasonality. Model your data with sophisticated processes, and forecast future outcomes

### Airline Modeling

A special - simplified - form of SARIMA model, but very often used in practice to model non-stationary seasonal time series.

### ARMAX Modeling

With ARMAX model, we can analyze time series data in conjunction with exogenous factors leading.

### X12-ARIMA

Supports the modeling and seasonal adjustment methodology for U.S. Census Bureau's X12-ARIMA in Excel.