I wish it wasn't out of desperation that I'm making this first post!(adsbygoogle = window.adsbygoogle || []).push({});

I have a neural network that is making predictions, the next 5 time points per training.

Back testing consists of appending these 5 point sets together to produce a data set that spans time over a much longer period.

The problem is that the results are pretty good except that there is too much "noise" present.

If the data set was periodic, I'd use DFT (discrete Fourier transform), toss the higher coefficients (now have LP filter) and move on.

However, its not periodic.

My thinking is I have 2 options:

- DFT tricks
- Other component methods: PCA, ICA, ???

I really don't want to get into polynomials or beam-fit splines. I'd rather stick with actual components of the data set.

Any suggestions would be greatly appreciated. So far I have burned over a man-week trying to make something work.

Thanks in advance,

Tom

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# First Post: How to Smooth End point of Finite Data Series time series

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