Complete the following steps to interpret a trend analysis. Key output includes the fitted trend equation, the accuracy measures, and the forecasts.

Examine the trend analysis plot to determine whether your model fits your data. If the fits closely follow the actual data, the model fits your data. Ideally, the data points should fall randomly around the fitted line.

- If the model fits the data, you can perform Double Exponential Smoothing and compare the two models.
- If the model does not does fit the data, perform the analysis again and select a different type of model. If you fit a linear model and see curvature in the data, select the quadratic, exponential, or S-curve model. If none of the models fit your data, use a different time series analysis. For more information, go to Which time series analysis should I use?.

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.

Examine the end of the trend analysis plot and the forecasts to determine whether the forecasts are likely to be accurate. The fits should follow the data closely, especially at the end of the series. If the fits start to shift away from the data at the end of the series, the underlying trend may be changing. If the trend is changing, the model might not generate accurate forecasts. In this case, collect more data to determine whether the trend over a longer period of time is less consistent.

Even if your forecasts appear to be accurate, be cautious about forecasts that are more than 3 periods in the future. Trends observed over a short span of data could be part of a larger cycle and may not persist into the future. Because trends can be volatile, you should usually only forecast 2 or 3 periods into the future.