Calculates the sum of the squared errors of the prediction function.
Syntax
SSE(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 forecasted 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 (e.g. $x_k$ or $\hat x_k$) in either time series will exclude the data point $(x_k,\hat x_k)$ from the SSE.
- The sum of the squared errors, $\mathrm{SSE}$, is defined as follows:
$$\mathrm{SSE}=\sum_{i=1}^N \left(x_i-\hat x_i \right )^2$$
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) |
|---|---|
| =SSE(SSE($B$1:$B1$9,$C$1:$C$19)) | SSE (51.375) |
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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