This is in contrast to both Wright's model and linear regression ( p-value < 0.0001). There was no difference between the ARIMA model and actual performance values ( p-value CT Add = 1.0, CT Sum=0.054). When the testing was repeated, the actual mean performance differed by 1.35 and 4.41 s for each of the tasks, respectively, from the ARIMA point forecast value. Using the mean absolute percentage error (MAPE) to measure forecast accuracy, the autoregressive integrated moving average (ARIMA) model predicted a further reduction in both CT Add to a mean of 51.51 ± 13.21 s (AIC = 5403.13) with an error of 6.32%, and CT Sum to a mean of 54.57 ± 15.37 s (AIC = 3852.61) with an error of 8.02% over an additional 100 forecasted trials. The median calculation time in seconds for adding 10 sequential six digit numbers, while that for summation (CT Sum) was 70 s (IQR = 14, range 53–108 s), and the difference between these times was statistically significant p < 0.0001. Addition and summation (addition combined with subtraction) using the Japanese Soroban computation system was undertaken over 60 days.
Time series forecasting may provide objective metrics for predictive performance in mental arithmetic. Department of Ophthalmology, Counties Manukau DHB, Auckland, New ZealandĪn ideal performance evaluation metric would be predictive, objective, easy to administer, estimate the variance in performance, and provide a confidence interval for the level of uncertainty.