Calculates the sum of absolute errors (SAE) between the forecast and the eventual outcomes.
Syntax
SAE(X, Y)
- X
- is the original (eventual outcomes) time series sample data (a one dimensional array of cells (e.g. rows or columns)).
- Y
- is the forecast time series data (a one dimensional array of cells (e.g. rows or columns)).
Remarks
- The time series is homogeneous or equally spaced.
- The two time series must be identical in size.
- A missing value (say $x_k$ or $\hat x_k$) in either time series will exclude the data point $(x_k,\hat x_k)$ from the SSE.
- The sum of absolute errors (SAE) or deviations (SAD), is defined as follows:
$$\mathrm{SAE}=\mathrm{SAD}=\sum_{i=1}^N \left | x_i-\hat x_i \right |$$
Where:
- $\{x_i\}$ is the actual observations time series
- $\{\hat x_i\}$ is the estimated or forecasted time series
Examples
Example 1:
|
|
| Formula | Description (Result) |
|---|---|
| =SAE($B$1:$B$19,$C$1:$C$19) | SAE (24.59) |
Files Examples
Related Links
References
- Hamilton, J .D.; Time Series Analysis , Princeton University Press (1994), ISBN 0-691-04289-6
- Tsay, Ruey S.; Analysis of Financial Time Series John Wiley & SONS. (2005), ISBN 0-471-690740
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