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![]() This forecasting method looks for seasonality patterns in the historical data and tries to determine the pattern that best matches the data. Since the data is monthly and repeats every 12 points, the detected seasonality was 12.In some cases we know what the seasonality length is, but in other cases we do not. We would expect to have a yearly cycle in this case, which would repeat itself every 12 points (months). Another example is hourly traffic data, where a seasonality of 24 points (hours) makes sense.In the example below you can see how a yearly seasonality was detected and applied in the forecast. An example of this could be ice cream sales presented in monthly data. Seasonality patternsIn many business scenarios there is a seasonality pattern that we would like to take into account in the forecast. Exponential Smoothing methods are a popular way to forecast and are among the leading methods that have become industry standards.The main advantages of using the ETS method are the ability to detect seasonality patterns and confidence intervals. Function Financial In Excel Full Seasonal CyclesThe default level of 95 percent confidence can be changed using the up or down arrows and can be used in two ways: A smaller interval implies more confidence in the prediction for the specific point. The confidence interval helps you figure out the accuracy of the prediction. In case the seasonal data was not significant enough to be detected, or you know which seasonality you expect, you can manually override the automatically detected value by selecting Set Manually.Apart from predicting future values for the input time series, the ETS forecast can also return a confidence interval.The confidence interval is the range surrounding each predicted value in which 95 percent of future points are expected to fall based on the forecast (with normal distribution). The automatically detected value in the Seasonality section can be found in the Create Forecast dialog under Options. It is recommended to have at least 2–3 full seasonal cycles in the historical data. Os x dmgNext, under the Data tab, click the Forecast sheet button. This provides an indication of how well the underlying model fits the historical data.To create a forecast sheet, first make sure you have your time-based series data set ready (it should have a time series and values series). You can experiment with several of the advanced options (such as how to account for missing points, seasonality, etc.) and observe if the previewed confidence interval got thinner or wider. There are several things you can do in order to understand how accurately your data is being forecasted:Look at the width of the confidence interval that was described above.Experiment with the Forecast From control by setting it to a date earlier than your last historical point. As data is rarely perfect, it’s important to investigate the forecast and understand the applicability of it in the case of your specific data. How do I know whether my data is forecasted accurately? Can trust it?As with many statistical tools, the accuracy of the forecast would depend on this input data. Alternatively, if you are comfortable with using sheet functions, you can do exactly the same using the new FORECAST.ETS* sheet functions, which are described here: Forecasting Functions Help. Using the functions allows you to use the exact same functionality. ![]()
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