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  2. Tracking signal - Wikipedia

    en.wikipedia.org/wiki/Tracking_signal

    where a t is the actual value of the quantity being forecast, and f t is the forecast. MAD is the mean absolute deviation. The formula for the MAD is: = | | where n is the number of periods. Plugging this in, the entire formula for tracking signal is:

  3. FAME (database) - Wikipedia

    en.wikipedia.org/wiki/FAME_(database)

    FAME Desktop Add-in for Excel: FAME Desktop is an Excel add-in that supports the =FMD(expression, sd, ed,0, freq, orientation) and =FMS(expression, freq + date) formulas, just as the 4GL command prompt does. These formulas can be placed in Excel spreadsheets and are linked to FAME objects and analytics stored on a FAME server. Sample Excel ...

  4. Mean percentage error - Wikipedia

    en.wikipedia.org/wiki/Mean_percentage_error

    where is the actual value of the quantity being forecast, is the forecast, and is the number of different times for which the variable is forecast. Because actual rather than absolute values of the forecast errors are used in the formula, positive and negative forecast errors can offset each other; as a result, the formula can be used as a ...

  5. Mean absolute percentage error - Wikipedia

    en.wikipedia.org/wiki/Mean_absolute_percentage_error

    It is a measure used to evaluate the performance of regression or forecasting models. It is a variant of MAPE in which the mean absolute percent errors is treated as a weighted arithmetic mean. Most commonly the absolute percent errors are weighted by the actuals (e.g. in case of sales forecasting, errors are weighted by sales volume). [3]

  6. Exponential smoothing - Wikipedia

    en.wikipedia.org/wiki/Exponential_smoothing

    Exponential smoothing or exponential moving average (EMA) is a rule of thumb technique for smoothing time series data using the exponential window function.Whereas in the simple moving average the past observations are weighted equally, exponential functions are used to assign exponentially decreasing weights over time.

  7. Autoregressive integrated moving average - Wikipedia

    en.wikipedia.org/wiki/Autoregressive_integrated...

    The order p and q can be determined using the sample autocorrelation function (ACF), partial autocorrelation function (PACF), and/or extended autocorrelation function (EACF) method. [10] Other alternative methods include AIC, BIC, etc. [10] To determine the order of a non-seasonal ARIMA model, a useful criterion is the Akaike information ...