STOCHASTIC CHARACTERISTICS OF DE- NOISING TIME SERIES

Astrophysical, geophysical, meteorological, and other types of physical data can be the result of an experiment, emerge as a signal from a dynamical system, or include sociological, economic, or biological data. It is always assumed that a certain level of noise will be present in time series data, regardless of its source. The analysis of all such data in the presence of noise frequently produces inaccurate results. The time series data filtering technique is a tool to remove as many of these bugs as we can and simply prepare the data for additional analysis. When you filter a time series, you keep some frequencies while removing the spectral strength at others. Both time series analysis and digital signal processing (DSP) make extensive use of filters in applications for DSP. Here, we tried to create an adjustable method of filtering a time series with the idea that the series had performed the necessary de-noising and modification updates. These qualities were determined in this study. We've proven analytically that the current model can effectively resist errors and preserve positional significance in the time series. When such data are analyzed in a noisy environment, the results are frequently misinterpreted. So, before we can begin the extensive investigation, we must first build an initial platform by de-identifying the data. It is usually necessary to deal with the problem of filtering a time series of data. In this work, we analysis the Fourier series with respect to the finite Fourier transform, resulting in the determination of the properties of this series. The outcomes of this study improve understanding of this series' characteristics.