I have been studying SG filters and i recently found another filter which seem to be commonly used in financial data smoothing which is the least-squares moving average, this filter is also called moving linear regression or time series forecast, here this filter is defined as :
The endpoint moving average (EPMA) establishes an average price by fitting a least squares straight line (see Linear Regression) through the past N days closing prices and taking the endpoint of the line (ie. the line as at the last day) as the average.
Which is similar to the SG filter in the sense that both locally fit a polynomial function using the method of least squares, however the SG filter appear smoother and more precise than the EPMA, so why are they appearing different ?
I might be wrong but my guess is that a 1st order SG filter using the coefficients show'n below :
will start convolution where N = 14 down to N = 0 while the EPMA will reverse the order of the convolution starting with N = 0 to N = 14, i don't know if i'am clear.