Associative Model (68): Forecasting Technique that uses explanatory variables to predict future demand.
Bias (93): Persistent tendency for forecasts to be greater or less than the actual values of a time series.
Centered Moving Average (83): A moving average positioned at the center of the data that were used to compute it.
Control Chart (92): A visual tool for monitoring forecast errors.
Correlation (88): A measure of the strength and direction of the relationship between two variables.
Cycle (70): Wavelike variations lasting more than one year.
Error (90): Difference between the actual value and the value that was predicted for a given period.
Exponential Smoothing (75): Weighted averaging method based on forecast plus a percentage of the forecast error.
Forecast (65): statement about the future value of a variable of interest.
Irregular Variation (70): Caused by unusual circumstances not reflective of typical behavior.
Judgmental Forecast (68): Forecasts that use subjective inputs such as opinions from consumer surveys, sales staff, managers, executives, and exports.
Least Squares Line (85): Minimizes the sum of the squared vertical deviations around the line.
Linear Trend Equation (77): Ft = a + bt, used to develop forecasts when
trend is present.
Mean Absolute Deviation (MAD) (90): the average absolute forecast error.
Mean Absolute Percent Error (MAPE) (90): the average of squared forecast errors.
Mean Squared Error (MSE) (90): The average absolute percent error.
Moving Average (72): Technique that averages a number of recent actual values, updated as new values become available.
Naïve Forecast: The forecast of any period equals the previous period’s actual value.
Predictor Variables (85): variables that can be used to predict values of the variable of interest.
Random Variations (70): Residual variations after all other behaviors are accounted for.
Regression (85): Technique for fitting a line to a set of points.
Seasonal Relative (82): Percentage of average or trend.
Seasonal Variations (81): Regular repeating movements in series values can be tied to recurring events.
Seasonality (70): Short term regular variations related to the calendar or time of day.
Time Series (70): A time-ordered sequence of observations taken at regular intervals.
Time Series Forecasts (68): Forecasts that project patterns identified n recent time-series observations.
Tracking Signal (93): The ratio of cumulative forecast error to the corresponding value of MAD, used to monitor a forecast.
Trend (70): a long term upward or downward movement in data.
Trend-Adjusted Exponential Smoothing (80): Variations of exponential smoothing used when a time series exhibits trend.
Weighted Average (74): More recent values in a series are given more weight in computing a forecast.