The optimal window size for a WMA is a tricky one: too big and the WMA lags behind the data and does not respond quickly. Too small, and the WMA is very noisy. I suggest you start with 4 weeks window and visually examine the WMA vs. the actual data, if it is too noise increase the window size.
Another approach is to compute the root-mean squared errors (RMSE) between the WMA and original data, and try to find the data that gives the smallest RMSE value. RMSE is a function in NumXL.
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The optimal window size for a WMA is a tricky one: too big and the WMA lags behind the data and does not respond quickly. Too small, and the WMA is very noisy. I suggest you start with 4 weeks window and visually examine the WMA vs. the actual data, if it is too noise increase the window size.
Another approach is to compute the root-mean squared errors (RMSE) between the WMA and original data, and try to find the data that gives the smallest RMSE value. RMSE is a function in NumXL.
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