%0 Journal Article
%T Noise Reduction in a Reputation Index
%A Peter Mitic
%J -
%D 2018
%R https://doi.org/10.3390/ijfs6010019
%X Abstract Assuming that a time series incorporates ˇ°signalˇ± and ˇ°noiseˇ± components, we propose a method to estimate the extent of the ˇ°noiseˇ± component by considering the smoothing properties of the state-space of the time series. A mild degree of smoothing in the state-space, applied using a Kalman filter, allows for noise estimation arising from the measurement process. It is particularly suited in the context of a reputation index, because small amounts of noise can easily mask more significant effects. Adjusting the state-space noise measurement parameter leads to a limiting smoothing situation, from which the extent of noise can be estimated. The results indicate that noise constitutes approximately 10% of the raw signal: approximately 40 decibels. A comparison with low pass filter methods (Butterworth in particular) is made, although low pass filters are more suitable for assessing total signal noise. View Full-Tex
%K reputation
%K reputation index
%K signal to noise
%K S/N
%K state-space
%K Kalman
%K time series
%K low pass filters
%K butterworth
%K moving average
%U https://www.mdpi.com/2227-7072/6/1/19