MDA - Mean Directional Accuracy

1. The time series is homogeneous or equally spaced.
2. The time series X and F must be of identical size
3. The time series X or F may include observations with missing values (e.g. #N/A or blank).
4. Observations with missing values in Y or F are excluded from the MDA calculation.
5. The MDA compares the forecast direction (upward or downward) to the actual realized direction
6. The mean directional accuracy is given by:
   
   $$ \mathrm{MDA} = \frac{1}{N}\sum_t \mathbf{1}_{sign(X_t - X_{t-1}) == sign(F_t - X_{t-1})} $$ 
   Where:
   
   • $\{X_i\}$ is the actual observations time series.
   • $\{F_i\}$ is the estimated or forecast time series.
   • $N$ is the number of non-missing data points.
   • $sign(\cdot)$ is sign function
   • $\mathbf{1}$ is the indicator function
7. In short, MDA provides the probability that the under study forecasting method can detect the correct direction of the time series.
8. MDA is a popular metric for forecasting performance in economics and finance.
9. The MDA function is available starting with version 1.66 PARSON.

Example 1:

Files Examples

Related Links

• Wikipedia - Mean Directional Accuracy

References

• R.J. Hyndman, A.B. Koehler, "Another look at measures of forecast accuracy", International Journal of Forecasting, 22 (2006), pp. 679-688
• James Douglas Hamilton; Time Series Analysis; Princeton University Press; 1st edition(Jan 11, 1994), ISBN: 691042896
• Tsay, Ruey S.; Analysis of Financial Time Series; John Wiley & SONS; 2nd edition(Aug 30, 2005), ISBN: 0-471-690740
• D. S.G. Pollock; Handbook of Time Series Analysis, Signal Processing, and Dynamics; Academic Press; Har/Cdr edition(Nov 17, 1999), ISBN: 125609906