Forum Discussion
Would you recommend the use of Excel complex numbers where performance is required?
The convolution seems to be pretty flexible in terms of having both a number of different applications and allowing experimentation to look at the effect of selecting different kernels for the smoothing. In my latest attempt I chose to use the binomial coefficients which I then looked up and found it is known as a Gaussian Filter, published in 1958.
Both the smoothness and the fit look pretty good to me, though Jon's dataset is so noisy that it is difficult to judge.
I was surprised by the work you published on GitHub. Whilst I had anticipated that there would be others working with FFT who would be far more knowledgeable than I (I had never even thought to look under the covers of the discrete form of Fourier transformations), I did not anticipate that any would be working with Excel! I wrongly assumed it would be all Python or MATLAB/Simulink.
I find it something of a surprise that it is possible to deploy quite advanced numerical algorithms as a spreadsheet formula. I suspect there is a long way to go before we see Excel solutions deployed to the full.
One thing that still troubles me is the limitation preventing the return of 'arrays of array' from helper functions. Most algorithms I implement require arrays of arrays and REDUCE/VSTACK is a cumbersome way of achieving what should be inbuilt functionality.
To have multidimensional arrays displayed as a two 2D array rather than mapped to 1D memory locations helps algorithm development because one dimension can be used to hold the input to each specific calculation whilst the other is free to to hold multiple instances of the calculation. That reduces the need for the complicated manipulation of indices.