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.

## 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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