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Add a comment. Sorted by: Reset to default. Highest score default Date modified newest first Date created oldest first. Community Bot 1 1 1 silver badge. So, sorry just making sure I understand. Obviously there is overfitting in this example, to avoid that you can adjust the number of harmonics in the model.
This good visual example demonstrates the weakness pointed out in the other posts: the frequency domain, by its nature, produces fixed cycles in the time domain. The red line extrapolation above is simpy a copy of the beginning segment of the blue observed line, albeit de-noised slightly. A "high" frequency threshold can be arbitrarily defined in relation to h. If the historical period had instead been initiated right before the hump at 50, then the very first predicted units would have that hump copy, which seems a bit silly and arbitrary.
Thus, by either eliminating and down-weighting the lower frequency components, we can reduce the arbitrariness induced by the historical data starting point. Maybe I misunderstand but that seems to be the best way to denoise. Show 1 more comment. It sounds like you want a combination of extrapolation and denoising.
Steve Tjoa Steve Tjoa 56k 16 16 gold badges 88 88 silver badges 98 98 bronze badges. So you are repeating most important coefficients in Z twice in the above example , and if use ifft to regenerate my time series, this new series will be longer than the original and by definition will have predictions in it. I guess by denoising you mean picking the most important coefficients.
If that's what you mean, then yes. And yes, preserving the most important coefficients will have a "smoothing" or "blurring" or "denoising" effect on the signal. What do you think? Yes, that is basically isolating a single complex sinusoid in the time domain. But the answer will be complex. You also need the component Y[N-ctr] in order to get a real signal. I read Black Swan. I am not talking about stock prices necessarily. Let's say..
So I am talking about something predictable. Let me clarify little further: Let's say I pinpointed a frequency that is pretty dominant in the data. How do I tie this back to points in the time domain data, so I can jump ahead and in the future and do prediction. Extrapolation is dangerous, yes. Usually, people make models, and assume that the future will behave like the model says.
Models depends on parameters, which are estimated with present or past observations. So you don't "predict" anything, you only fit a model. Alexandra C: I am talking about data that is known to repeat itself. Pablo Pablo 2, 3 3 gold badges 23 23 silver badges 39 39 bronze badges. Hi the link you have provided is broken. Can you please post again if possible or If you have? Thank you. DhivyaNarayanasamy fixed ; — Pablo. Very interesting but wouldn't you want to bias the prediction with more weight on the last half more recent values given?
That is try this with data that is a flat line for the first half but a slopped line for the other half. The sloped line should have more emphasis that the flat part. Sign up or log in Sign up using Google. Author Post time Subject Ascending Descending. Indicator is just a tool. Use it only if it can benefit you. Leave it if you don't know how to use it optimally. Best regards. Is the custom tf what is new? Mladen,Congrats for You and for All for this new forum.
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