Interpret the key results for Single Exponential Smoothing

Complete the following steps to interpret a single exponential smoothing analysis. Key output includes the smoothing plot, the accuracy measures, and the forecasts.

Step 1: Determine whether the model fits your data

Examine the smoothing plot to determine whether your model fits your data. If the fits closely follow the actual data, the model fits your data.
  • If the model fits the data, you can perform Moving Average and compare the two models.
  • If the model does not fit the data, examine the plot for trends or seasonality. If you see evidence of these, you should use a different time series analysis. For more information, go to Which time series analysis should I use?.

On this smoothing plot, the fits closely follow the data, which indicates that the model fits the data.

Step 2: Compare the fit of your model to other models

Use the accuracy measures (MAPE, MAD, and MSD) to compare the fit of your model to other time series models. These statistics are not very informative by themselves, but you can use them to compare the fits obtained by using different methods. For all 3 statistics, smaller values usually indicate a better fitting model. If a single model does not have the lowest values for all 3 statistics, MAPE is usually the preferred measurement.
Note

The accuracy measures provide an indication of the accuracy you might expect when you forecast out 1 period from the end of the data. Therefore, they do not indicate the accuracy of forecasting out more than 1 period. If you're using the model for forecasting, you shouldn't base your decision solely on accuracy measures. You should also examine the fit of the model to ensure that the forecasts and the model follow the data closely, especially at the end of the series.

Model 1

Accuracy Measures MAPE 8.1976 MAD 3.6215 MSD 22.3936

Model 2

Accuracy Measures MAPE 6.9551 MAD 2.7506 MSD 11.2702
Key Results: MAPE, MAD, MSD

In these results, all three numbers are lower for the 2nd model compared to the 1st model. Therefore, the 2nd model provides the better fit.

Step 3: Determine whether the forecasts are accurate

Examine the fits and the forecasts in the plot to determine whether the forecasts are likely to be accurate. The forecasts should follow the general flow of the data at the end of the series. Because the forecasts from single exponential smoothing are constant, it is important that there is no trend in the data before the forecasts. If there is a trend before the forecasts, the forecasts may not be accurate.

The forecasts from single exponential smoothing are very conservative because they are based solely on the latest estimate of the level, and no estimate of the trend. You should usually only forecast 6 periods into the future.

On this smoothing plot, the fits closely follow the data, especially at the end of the series. You can expect sales to be around 58 for the next 6 months with a range of approximately 52 and 65.

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