100% accuracy in predictions in a big percentage of known series database (OEIS). Other methods Granger causality Simulation Prediction market Probabilistic forecasting and Ensemble forecasting Forecasting accuracy The forecast error (also known as a residual) is the difference between the actual value and the forecast value for the corresponding period: where E is the forecast error at period t, Y is the actual value at period t, and F is the forecast for period t. A good forecasting method will yield residuals that are uncorrelated. If there are correlations between residual values, then there is information left in the residuals which should be used in computing forecasts. This can be accomplished by computing the expected value of a residual as a function of the known past residuals, and adjusting the forecast by the amount by which this expected value differs from zero. A good forecasting method will also have zero mean. If the residuals have a mean other than zero, then the forecasts are biased and can be improved by adjusting the forecasting technique by an additive constant that equals the mean of the unadjusted residuals. Measures of aggregate error: Scaled-dependent errors The forecast error, E, is on the same scale as the data, as such, these accuracy measures are scale-dependent and cannot be used to make comparisons between series on different scales. Mean absolute error (MAE) or mean absolute deviation (MAD): Mean squared error (MSE) or mean squared prediction error (MSPE): Root mean squared error (RMSE): Average of Errors (E): Percentage errors These are more frequently used to compare forecast performance between different data sets because they are scale-independent. However, they have the disadvantage of being extremely large or undefined if Y is close to or equal to zero. Mean absolute percentage error (MAPE): Mean absolute percentage deviation (MAPD): Scaled errors Hyndman and Koehler (2006) proposed using scaled errors as an alternative to percentage errors. Mean absolute scaled error (MASE): where m=seasonal period or 1 if non-seasonal Other measures Forecast skill (SS): Business forecasters and practitioners sometimes use different terminology. They refer to the PMAD as the MAPE, although they compute this as a volume weighted MAPE. For more information, see Calculating demand forecast accuracy. When comparing the accuracy of different forecasting methods on a specific data set, the measures of aggregate error are compared with each other and the method that yields the lowest error is preferred. Training