Returns the general-form exponential smoothing (out-of-sample) forecast estimate.

## Syntax

**GESMTH**(

**X**,

**Order**,

**Alpha**,

**Beta**,

**Gamma**,

**Phi**,

**Lambda**,

**Trend Type**,

**Seasonality Type**,

**L**,

**Optimize**,

**Adjust**,

**Log**,

**T**,

**Return Type**)

- X
- is the univariate time series data (a one-dimensional array of cells (e.g. rows or columns)).
- Order
- is the time order in the data series (i.e. the first data point's corresponding date (earliest date=1 (default), latest date=0)).
Order Description 1 ascending (the first data point corresponds to the earliest date) (default) 0 descending (the first data point corresponds to the latest date) - Alpha
- is the level smoothing factor (alpha should be between zero(0) and one(1) (exclusive)). If missing or omitted, 0.333 value is used.
- Beta
- is the trend smoothing factor (beta should be between zero(0) and one(1) (exclusive)). If missing or omitted, 0.333 value is used.
- Gamma
- is the seasonal change smoothing factor (gamma should be between zero(0) and One(1) (exclusive)). If missing or omitted, 0.50 value is used.
- Phi
- is the damping coefficient of the (Pegel) trend component (phi should be between zero(0) and one(1) (inclusive)). If missing or omitted, 1.0 value is used.
- Lambda
- is the coefficient value for the residuals autocorrelation adjustment term (lambda should be between negative one(-1) and one(1) (exclusive)). If missing or omitted, 0 value is used.
- Trend Type
- is a number that specify the trend component in the model: 0 (or missing) =None, 1=Additive, 2=Additive damped, 3=Multiplicative, 4=Multiplicative damped trend.
Value Description 0 None (default) 1 Additive trend (default) 2 Additive damped trend 3 Multiplicative trend 4 Multiplicative damped trend - Seasonality Type
- is a number that specify the seasonality component in the model: 0 (or missing) =None, 1= Additive Seasonality, 2=Multiplicative Seasonality.
Value Description 0 None (default) 1 Additive seasonality 2 Multiplicative seasonality - L
- is the season Length or duration in units of steps.
- Optimize
- is a flag (True/False) for searching and using optimal value of the smoothing factor. If missing or omitted, optimize is assumed False.
- Adjust
- is a flag (True/False) for adjusting for the autocorrelation (Chatfield) in forecast errors. If missing or omitted, Adjust is assumed False.
- Log
- Is a flag (True/False) for taking the natural logarithm of the input data prior to smoothing. If missing or omitted, Log is assumed False.
- T
- is the forecast time/horizon beyond the end of X. If missing, a default value of 0 (Latest or end of X) is assumed.
- Return Type
- is a number that determines the type of return value:0(or missing)=Forecast,1=Alpha,2=Beta,3=Gamma,4=phi,5=lambda,6=level component(series),7=trend component(series),8=seasonal component(series),9=adjustments (series),10=one-step forecasts(series).
Return Type Description 0 or omitted Forecast value 1 Level smoothing parameter (alpha) 2 Trend smoothing parameter (beta) 3 Seaonal smoothing parameter (gamma) 4 damping coefficient (phi) 5 autocorrelation correction coefficient (lambda) 6 level component (series) 7 trend component (series) 8 seasonal component (series) 9 error autocorrelation (Chatfield) adjustment component (series) 10 one-step forecasts (series)

## Remarks

- The time series is homogeneous or equally spaced.
- The time series may include missing values (e.g. #N/A) at either end.
- The GESMTH is a generalization of the triple exponential smoothing model. The recursive form of the single-seasonality general exponential smoothing equation is expressed as follows:

$$ \begin{array}{l} \hat{F}_t(m)=f(S_t,b_t,C_{t-L+m})+\lambda e_{t-1}\\ \\ S_t=\alpha\times F_1(X_t,C_{t-L})+(1-\alpha)F_2(S_{t-1},b_{t-1})\\ b_t=\beta\times G_1(S_t,S_{t-1})+(1-\beta)G_2(b_{t-1})\\ C_t=\gamma\times H_1(X_t,S_t)+(1-\gamma)C_{t-L}\\ \\ e_t=X_t-f(S_t,b_t,C_{t-L+m}) \end{array} $$ - The functional form of $F_2(.),G_1(.), \mathrm{and}\, G_2(.)$ are defined by the type of the trend in the model:

No Trend $\begin{array}{l} F_2(S_{t-1},b_{t-1})=S_{t-1}\\ G_1(S_t,S_{t-1})=0\\ G_2(b_{t-1})=0 \end{array} $ Additive Trend $ \begin{array}{l} F_2(S_{t-1},b_{t-1})=S_{t-1}+b_{t-1}\\ G_1(S_t,S_{t-1})=S_t - S_{t-1}\\ G_2(b_{t-1})=b_{t-1} \end{array} $ Damped Additive Trend $ \begin{array}{l} F_2(S_{t-1},b_{t-1})=S_{t-1}+\phi b_{t-1}\\ G_1(S_t,S_{t-1})=S_t - S_{t-1}\\ G_2(b_{t-1})=\phi b_{t-1} \end{array} $ Multiplicative Trend $ \begin{array}{l} F_2(S_{t-1},b_{t-1})=S_{t-1}\times b_{t-1}\\ G_1(S_t,S_{t-1})=S_t / S_{t-1}\\ G_2(b_{t-1})= b_{t-1} \end{array} $ Damped Multiplicative Trend $ \begin{array}{l} F_2(S_{t-1},b_{t-1})=S_{t-1}\times b_{t-1}^\phi\\ G_1(S_t,S_{t-1})=S_t / S_{t-1}\\ G_2(b_{t-1})= b_{t-1}^\phi \end{array} $ - The functional form of $F_1(.)$ and $H(.)$ are defined by the type of the seasonality type:

No Seasonality $\begin{array}{l} F_1(X_t, C_{t-L})=X_t\\ H(X_t, S_t)=0 \end{array} $ Additive Seasonality $\begin{array}{l} F_1(X_t, C_{t-L})=X_t - C_{t-L}\\ H(X_t, S_t)=X_t - S_t \end{array} $ Multiplicative Seasonality $\begin{array}{l} F_1(X_t, C_{t-L})=X_t / C_{t-L}\\ H(X_t, S_t)=X_t / S_t \end{array} $ - The functional form of the m-step (out-of-sample) forecast ($f(S_t,b_t,C_{t-L+m})$) is determined by the trend type and the seasonality type:
No Seasonality Additive Multiplicative **No Trend**$S_t$ $S_t + C_{t-L+m}$ $S_t \times C_{t-L+m}$ **Additive Trend**$S_t+ m\times b_t$ $S_t + m\times b_t+C_{t-L+m}$ $(S_t +m\times b_t) \times C_{t-L+m}$ **Damped Additive Trend**$S_t+ (\phi+\cdots+\phi^m)b_t$ $S_t + (\phi+\cdots+\phi^m)b_t + C_{t-L+m}$ $(S_t + (\phi+\cdots+\phi^m) b_t) \times C_{t-L+m}$ **Multiplicative Trend**$S_t \times b_t^m$ $S_t \times b_t^m + C_{t-L+m}$ $S_t \times b_t^m \times C_{t-L+m}$ **Damped Multiplicative Trend**$S_t \times b_t^{\phi+\cdots+\phi^m}$ $S_t \times b_t^{\phi+\cdots+\phi^m} + C_{t-L+m}$ $S_t \times b_t^{\phi+\cdots+\phi^m} \times C_{t-L+m}$ - The last term in the forecast function ($\hat{F}_t(m)$)is the forecast error 1st order autocorrelation adjustment term ($\lambda e_{t-1}$),but this remains the same for all trend and seasonality types.
- In sum, the GESMTH function captures the 15-different models for the single seasonality exponential smoothing, and the Bown’s simple exponential, holt’s double exponential and Holt-Winters triple exponential are merely 3-special cases in GESMTH.
- The (damped) multiplicative trend can only be used with positive valued input time series.
- The three components equations in GESMTH are interdependent in sense that three components must be updated each period
- Similar to Holt-Winters smoothing, the smoothing coefficient $\alpha$ is again used to control speed of adaptation to local level, a second smoothing constant $\beta$ is used to control the degree of a local trend, a third smoothing constant $\gamma$ is introduced to control the degree of local seasonal Indices, and finally, a fourth constant $\phi$ to dampen the trend carried through to multi-step-ahead forecast periods.
- The GESMTH calculate a point forecast. There is no probabilistic model assumed for the simple exponential smoothing, so we can’t derive a statistical confidence interval for the computed values.
- In practice, the Mean Squared Error (MSE) for prior out-of-sample forecast values are often used as a proxy for the uncertainty (i.e. variance) in the most recent forecast value.
- Similar to other recursive exponential smoothing models, the GESMTH method requires a set of starting values. The nature of value and how we calculate them depend on the specified trend and seasonality types
- For non-seasonal time series (Seasonality type = None), we need starting value for level ($S_1$) and trend ($b_1$), if trend is chosen. The calculation of the initial value depends on the trend type (none, vs. additive vs. multiplicative).
- For seasonal time series (additive and multiplicative), we need starting values for the seasonal indices ($C_{1\cdots L}$), initial level ($S_1$), and initial trend ($b_1$), if trend is chosen.
- The actual calculation of the initial values can be summarized in 3 steps
- Seasonally-adjust the data to estimate trend series.
- Detrend the data to estimate the seasonal indices.
- Average over multiple seasons to improve the estimate.

- Starting from NumXL version 1.65, the GESMTH has a built-in optimizer to find the best value of ($\alpha,\beta,\phi,\gamma$, and $\lambda$) that minimize the SSE (loss function ($U(.)$)) for the one-step forecast calculated in-sample.

$$ \begin{array}{l} U(\alpha,\beta,\phi,\gamma,\lambda)=\mathrm{SSE}=\sum_{t=1}^{N-1}(X_{t+1}-\hat{F}_t(1))^2\\ \\ \min_{\alpha,\beta,\phi,\gamma \in (0,1)} U(\alpha,\beta,\phi,\gamma,\lambda) \end{array} $$ - For initial values, the NumXL optimizer will use the input value of ($\alpha,\beta,\phi,\gamma$, and $\lambda$) (if available) in the minimization problem, and the initial values for the level, trend and seasonal indices series ($S_1,b_1,C_{1\cdots L}$) are computed from the input data.
- The GESMTH function return the found optimal value for $\alpha,\beta,\phi,\gamma$, and $\lambda$, and the corresponding one-step smoothing series of level, trend, seasonal indices, adjustment terms and the one-step (in-sample) forecast series.
- For seasonal model, the input time series must have at least two full seasons of observations to use the built-in optimizer.
- NumXL implements the spectral projected gradient (SPG) method for finding the minima with a boxed boundary
- The SPG requires loss function value and the gradient ($\nabla$). NumXL implements the exact derivative formula (vs. numerical approximation) for performance purposes.

$$ \begin{array}{l} \nabla U = \frac{\partial U}{\partial \alpha} \vec{e_\alpha} + \frac{\partial U}{\partial \beta} \vec{e_\beta} + \frac{\partial U}{\partial \phi} \vec{e_\phi} \frac{\partial U}{\partial \gamma} \vec{e_\gamma} \frac{\partial U}{\partial \lambda} \vec{e_\lambda}\\ \\ \frac{\partial U}{\partial \alpha} = -2\times\sum_{t=1}^{N-1}(X_{t+1}-\hat{F}_t(1))\times \frac{\partial \hat{F}_t}{\partial \alpha}\\ \frac{\partial U}{\partial \beta} = -2\times\sum_{t=1}^{N-1}(X_{t+1}-\hat{F}_t(1))\times \frac{\partial \hat{F}_t}{\partial \beta}\\ \frac{\partial U}{\partial \phi} = -2\times\sum_{t=1}^{N-1}(X_{t+1}-\hat{F}_t(1))\times \frac{\partial \hat{F}_t}{\partial \phi}\\ \frac{\partial U}{\partial \gamma} = -2\times\sum_{t=1}^{N-1}(X_{t+1}-\hat{F}_t(1))\times \frac{\partial \hat{F}_t}{\partial \gamma}\\ \frac{\partial U}{\partial \lambda} = -2\times\sum_{t=1}^{N-1}(X_{t+1}-\hat{F}_t(1))\times e_{t-1} \end{array} $$ - Internally, during the optimization, NumXL computes recursively both the smoothed time series, levels, trends, seasonal indices and all the in-sample derivatives, which are used for the loss function and its derivative.
- The SPG is an iterative (recursive) method, and it is possible that the minima can’t be found the within allowed number of iterations and/or tolerance. In this case, NumXL will not fail, instead NumXL uses the best alpha found so far.
- The SPG has no provision to detect or avoid local minima trap. There is no guarantee of global minima.

- The SPG requires loss function value and the gradient ($\nabla$). NumXL implements the exact derivative formula (vs. numerical approximation) for performance purposes.
- In general, the SSE function in GESMTH yields a continuous smooth non-monotone curve, that SPG minimizer almost always finds an optimal solution in a very few iterations
- The GMSE function is available starting with version 1.65 HAMMOCK.

## Files Examples

## Related Links

## References

- 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

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