Usage of SVR in Non Stationary Time Series Forecasting Based on my knowledge of Support Vector Regression(SVR), One of the assumptions of SVR are

*

*Independent Identical Distribution.

So the application of SVR in forecasting of non-stationary Time series makes sense? As the properties of non-stationary is in violation of the basic assumptions of SVR.

The condition of independent identical distributions (iid) refers to the $(x,y)$ data points. Independence means:
$$
p((x_i, y_i), (x_k, y_k)) = p((x_i, y_i)) p((x_k, y_k)),
$$
i.e. the sampling of $(x_i, y_i)$ doesn't give us any information about the sampling of $(x_k, y_k)$, given the distribution.
And the identical distribution part says, that $(x_i, y_i)$ and $(x_k, y_k)$ have been sampled from the same distribution.
On the other hand, the stationarity requirement in time series, where the $x_i$ are just consecutive integers, is referring to the mean and autocovariance of the $y$ values, requiring those to be constant. In particular, $y_i$ and $y_k$ can still be dependent, as long as this dependence doesn't change.
Thus, both non-stationary and stationary time series (usually) violate the iid requirement.
