Yt = Trend × Seasonal × Error
Term | Description |
---|---|
Yt | observation at time t |
Yt = Trend + Seasonal + Error
Term | Description |
---|---|
Yt | observation at time t |
The data can be detrended by either dividing the data by the trend component (multiplicative model) or subtracting the trend component from the data (additive model).
Decomposition calculates the forecast as the linear regression line multiplied by (multiplicative model) or added to (additive model) the seasonal indices. Data prior to the forecast origin are used for the decomposition.
Mean absolute percentage error (MAPE) measures the accuracy of fitted time series values. MAPE expresses accuracy as a percentage.
Term | Description |
---|---|
yt | actual value at time t |
fitted value | |
n | number of observations |
Mean absolute deviation (MAD) measures the accuracy of fitted time series values. MAD expresses accuracy in the same units as the data, which helps conceptualize the amount of error.
Term | Description |
---|---|
yt | actual value at time t |
fitted value | |
n | number of observations |
Mean squared deviation (MSD) is always computed using the same denominator, n, regardless of the model. MSD is a more sensitive measure of an unusually large forecast error than MAD.
Term | Description |
---|---|
yt | actual value at time t |
fitted value | |
n | number of observations |