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

ARMA Modeling

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

ARIMA Modeling

ARIMA Modeling

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

SARIMA Modeling

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

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

Airline Modeling

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

ARMAX Modeling

ARMAX Modeling

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

X12-ARIMA

X12-ARIMA

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