An Auto-Regressive Formulation for Smoothing and Moving Mean with Exponentially Tapered Windows

More robust approaches are studied to this effect Data smoothing is heavily utilized in many learning [41]-[43], where a prominent example is the moving median problems to detect certain patters in the data or mitigate filter. Although the moving median is a nonlinear smoother noisy/anomalous values [1]. The notion of smoothing stems and the smoothed signal is acquired by the weighted median from the inference that the data points close to each other in of close groups of samples, it is efficiently computable by their features should have close mappings (functional evaluations), use of indexable skiplist [44]. We point out that specifically i.e., behave similarly. The most prominent example is for the moving median, the smoothed signal is statistically a time-series dataset where data points closer in time should optimal in the maximum likelihood sense, when the signal have similar values, e.g, unnaturally high or low values should contamination results from a Laplace distribution [45]. The be suppressed to match the nominal data pattern. Smoothing moving median filter has many applications including mass is mainly done on the datasets for easier extraction of their spectrometry [46], edge detection [47]. The application is information and consequently ease their analyses [2]. This especially rich in the field of image processing because of approach is applied in many fields including signal processing the edge-preserving capabilities [48].