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Seasonality is a number for the length (number of points) of the seasonal pattern and is automatically detected.
For example, in a yearly sales cycle, with each point representing a month, the seasonality is 12.
When your data contains multiple values with the same timestamp, Excel will average the values.
To use another calculation method, such as Median or Count, pick the calculation you want from the list.
The confidence interval is the range surrounding each predicted value, in which 95% of future points are expected to fall, based on the forecast (with normal distribution).
Confidence interval can help you figure out the accuracy of the prediction.
If you have historical time-based data, you can use it to create a forecast.
When you create a forecast, Excel creates a new worksheet that contains both a table of the historical and predicted values and a chart that expresses this data.
Doing this adds a table of statistics generated using the FORECAST. STAT function and includes measures, such as the smoothing coefficients (Alpha, Beta, Gamma), and error metrics (MASE, SMAPE, MAE, RMSE).
To handle missing points, Excel uses interpolation, meaning that a missing point will be completed as the weighted average of its neighboring points as long as fewer than 30% of the points are missing.
To treat the missing points as zeros instead, click Zeros in the list.
It’s okay if your timeline series is missing up to 30% of the data points, or has several numbers with the same time stamp. However, summarizing data before you create the forecast will produce more accurate forecast results. When you pick a date before the end of the historical data, only data prior to the start date are used in the prediction (this is sometimes referred to as "hindcasting").
Check or uncheck Confidence Interval to show or hide it.
Note: The timeline requires consistent intervals between its data points.