XLMiner facilitates the analysis of datasets via the use of trend discovery techniques (autocorrelation and partial autocorrelation) and comprehensive modeling methods (ARIMA and exponential smoothing).
ARIMA — AutoRegressive Integrated Moving-Average model — is one of the most popular modeling methods used in time series forecasting, due largely to its focus on using data autocorrelation techniques to achieve high-quality models. XLMiner fully utilizes all aspects of ARIMA implementation, including variable selections, seasonal / non-seasonal parameter definitions, and advanced options such as iteration maximums, output, and forecast options.
ARIMA Modeling in XLMiner
An ARIMA model is a regression-type model that includes autocorrelation. When estimating ARIMA coefficients, the basic assumption is that the data is stationary; meaning, the trend or seasonality cannot affect the variance. This is generally not true. In order to achieve stationary data, XLMiner needs to apply differencing: ordinary, seasonal, or both.
After XLMiner fits the model, various results will be available. The quality of the model can be evaluated by comparing the time plot of the actual values with the forecasted values. If both curves are close, then it can be assumed that the model is a good fit. The model should expose any trends and seasonality, if any exist.
Next an analysis of the residuals should convey whether or not the model is a good fit: random residuals means that the model is accurate, but if the residuals exhibit a trend then the model may be inaccurate. Fitting an ARIMA model with parameters (0,1,1) will give the same results as exponential smoothing, while using the parameters (0,2,2) will give the same results as double exponential smoothing.
How to Access ARIMA Settings in Excel
- Launch Excel.
- In the toolbar, click XLMINER PLATFORM.
- In the ribbon, click ARIMA.
- In the drop-down menu, select ARIMA Model.
ARIMA Model Summary
- ARIMA: AutoRegressive Integrated Moving Average.
- Forecasting model used in time-series analysis.
- ARIMA Parameter Syntax: ARIMA (p,d,q) where p = the number of auto-regressive terms, d = the number of non-seasonal differences, and q = the number of moving average terms.
- Time Series Example: View an example of how an ARIMA model can be applied.
- Using Time Series: How to use time series analysis functionality within XLMiner.
- Smoothing Models: How smoothing techniques can be applied to time series forecasting models.
- XLMiner Online Help: Help system covering functionality within the XLMiner module.