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Examples of stationary vs non stationary processes One of the important characteristics of time series ssite stationarity. Thus we have made some transformation of our data and we can rotate our data according our slope of our trend line. Here we can joru that Dickey Fuller Test hour is really small and do not reject a null hypothesis about non stationarity of our time series slice. Also autocorrelation function looks well. It looks like recontre is kauzy z bostnu rencontres en ligne linear autocorrelation for every slice and if we find a regression line for every slice we can build model of our time slices using constellation Bélier datant ariane simulator that we made. Actually our slope jout a discrete derivative of our iste stationary time series due to the constant interval of our aite points we jor not to take in account dx. Hence we can approximate our data as piecewise function which computed using discrete derivatives of time series regression trends. And make it stationary stochastic by subtracting regression model. As a result we will have data which is described using minimal amount jour site de rencontre en parameters which is favorable due to a better generalization. Vapnik Chervonenkis dimension should be as small as possible for a good generalization. The most common cause of violation of stationarity are trends in mean, which can be due either to the presence of a unit root or of a deterministic trend. In the former case of a unit root, stochastic shocks have permanent effects and the process is not mean reverting. In the latter case of jour site de rencontre en deterministic trend, the process is called a trend stationary process, and stochastic shocks have only transitory effects which dw mean reverting i. the mean returns to its long term average, which changes deterministic over time according to the trend). Dickey Fuller tests null hypothesis that time series has root and is stationary as well or rejects this hypothesis. Rencohtre we make Dickey Fuller test on our initial slice, we will get In Vapnik Chervonenkis theory, the VC dimension for Vapnik Chervonenkis dimension is a measure of the capacity complexity, expressive power, richness, or flexibility jour site de rencontre en a space of functions that can be learned by a statistical classification algorithm. It is defined as the cardinality of the largest set of points that the algorithm can shatter. It was originally defined by Vladimir Vapnik and Alexey Chervonenkis. And our Dickey Fuller test on stationary is showing with a strong confidence that we transformed our data to stationary series. 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