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When I apply differerent thresholding, wavelet denosing functions to non stationary time series which has been detrended via Loess regression and demean it. I expect that when this processed series are submited to denoising / thresholding will result in a clean series with smaller values than the submited signal and not to values at any point which are above those of the signal, Is my thinking correct.

On another hand could it be thoughthought as per those values in a procesed signal whichwhich lie above the processed signal as if the procesed signal would ought to be above those levels and is not due to an anomaly instead than below those levels. Of course the functions that described the signal is an aproximation so fiting erros should have to be expected.

When I apply differerent thresholding, wavelet denosing functions to non stationary time series which has been detrended via Loess regression and demean it. I expect that when this processed series are submited to denoising / thresholding will result in a clean series with smaller values than the submited signal and not to values at any point which are above those of the signal, Is my thinking correct.

On another hand could it be though as per those values in a procesed signal which lie above the processed signal as if the procesed signal would ought to be above those levels and is not due to an anomaly instead than below those levels.

When I apply differerent thresholding, wavelet denosing functions to non stationary time series which has been detrended via Loess regression and demean it. I expect that when this processed series are submited to denoising / thresholding will result in a clean series with smaller values than the submited signal and not to values at any point which are above those of the signal, Is my thinking correct.

On another hand could it be thought as per those values in a procesed signal which lie above the processed signal as if the procesed signal would ought to be above those levels instead than below those levels. Of course the functions that described the signal is an aproximation so fiting erros should have to be expected.

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Denoising / thresholding via wavelets

When I apply differerent thresholding, wavelet denosing functions to non stationary time series which has been detrended via Loess regression and demean it. I expect that when this processed series are submited to denoising / thresholding will result in a clean series with smaller values than the submited signal and not to values at any point which are above those of the signal, Is my thinking correct.

On another hand could it be though as per those values in a procesed signal which lie above the processed signal as if the procesed signal would ought to be above those levels and is not due to an anomaly instead than below those levels.