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Automatic Time Series Forecasting

The ESM procedure provides a quick way to generate forecasts for many time series or transactional data in one step by using exponential smoothing methods. All parameters associated with the forecasting model are optimized based on the data.

You can use the following smoothing models:

  • simple

  • double

  • linear

  • damped trend

  • seasonal

  • Winters method (additive and multiplicative)

Additionally, PROC ESM can transform the data before applying the smoothing methods using any of these transformations:

  • log

  • square root

  • logistic

  • Box-Cox

In addition to forecasting, the ESM procedure can also produce graphic output.

The ESM procedure can forecast both time series data, whose observations are equally spaced at a specific time interval (for example, monthly, weekly), or transactional data, whose observations are not spaced with respect to any particular time interval. (Internet, inventory, sales, and similar data are typical examples of transactional data. For transactional data, the data are accumulated based on a specified time interval to form a time series.)

The ESM procedure is a replacement for the older FORECAST procedure. ESM is often more convenient to use than PROC FORECAST but it supports only exponential smoothing models.

The FORECAST procedure provides forecasting of univariate time series using automatic trend extrapolation. PROC FORECAST is an easy-to-use procedure for automatic forecasting and uses simple popular methods that do not require statistical modeling of the time series, such as exponential smoothing, time trend with autoregressive errors, and the Holt-Winters method.

The FORECAST procedure supplements the powerful forecasting capabilities of the econometric and time series analysis procedures described previously. You can use PROC FORECAST when you have many series to forecast and you want to extrapolate trends without developing a model for each series.

The FORECAST procedure includes the following features:

  • choice of the following forecasting methods:

    • EXPO method—exponential smoothing: single, double, triple, or Holt two-parameter smoothing

    • exponential smoothing as an ARIMA Model

    • WINTERS method—using updating equations similar to exponential smoothing to fit model parameters

    • ADDWINTERS method—like the WINTERS method except that the seasonal parameters are added to the trend instead of multiplied with the trend

    • STEPAR method—stepwise autoregressive models with constant, linear, or quadratic trend and autoregressive errors to any order

    • Holt-Winters forecasting method with constant, linear, or quadratic trend

    • additive variant of the Holt-Winters method

  • support for up to three levels of seasonality for Holt-Winters method: time-of-year, day-of-week, or time-of-day

  • ability to forecast any number of variables at once

  • forecast confidence limits for all methods

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