What are the various forms of heteroscedasticity? Is this picture below indicating a form of heteroscedasticity? According to the definition of heteroscedasticity, heteroscedasticity exists when the variance is not the same. But here the variance is the same but the average increases.

 A: Non-constant variance in the residuals can arise as the result of different Gaussian violations. For example if the residual series has Pulses/Seasonal Pulses/Level Shifts or Local Time trends the variance of the errors can exhibit non-constancy. If there is auto-correlation in the residuals then this will lead to a non-constant error variance . If the model parameters vary over time this can lead to non-constant error variance. If the error variance is proportional to the expected value then this leas to non-constant error variance. If the error variance changes deterministically at different points in time this also is a de facto case of non-constant variance. If the variance of the errors can be represented by an ARIMA model this is also a case of non-const variance. The conclusion is that one needs to evaluate some of the alternative states-of-nature in order to determine that approach that is minimally sufficient. As @nick states one might need to actually have more information and nearly always in my opinion the raw data.
A: The fact that your residuals are trending might suggest an issue of autocorrelation rather than heteroskedasticity (whether they are heteroskedastic or not, which they may well be and which could be tested by, say, White's test),
