I realize this is a late answer to this post, but it still makes the top two to three results on Google for "ensemble average" and an answer has not yet been officially accepted. For posterity, I figured I would try to answer it to the best of my ability in the way that the question has been phrased.

First, it is important to have a broad understanding of what a **stochastic process** is. It is a fairly simple concept, analogous to a random variable. However, where the value of a random variable can take on certain *numbers* with various probabilities, the "values" of stochastic processes manifest as certain *waveforms* (again, with various probabilities). As an example in the discrete world, the outcome of a coin flip could be viewed as a random variable - it can take on two values with roughly equal probability. However, if you recorded the outcome of *n* coin flips (where *n* could be any whole number, up to infinity), and were to do so many times, you could view this "set of *n* coin flips" as a stochastic process. Results where roughly half are heads and half tails would have relatively high probabilities, while results where almost all are heads or almost all tails would have relatively low probabilities. Obviously, there are also continuous random variables and stochastic processes can be either discrete or continuous for both axes (time/trials vs. values/outcomes of each trial).

It is also important to understand **expected value**. This is even simpler - it's the *value* that, over a long period of time/many trials, you would *expect* your random variable to have. It's the mean. The average. Integrate/sum over all time/trials and divide by the amount of time/number of trials.

Now that these two things are covered, the **ensemble average** of a stochastic process can be explained in simple language and mathematical terms. In the simplest sense, the ensemble average is analogous to expected value. That is, given a large number of trials, it is the "average" waveform that would result from a stochastic process. Note that this means that an ensemble average is a function of the same variable that the stochastic process is. Mathematically, it can be denoted as:

$$ E[X(t)] = \mu_X(t) = \int_{-\infty}^\infty x*p_{X(t)}(x)dx $$

where $p_{X(t)}$ is the PDF of $X(t)$.

You also mentioned the **time average** for a stochastic process. This is a very different thing, which itself is actually a random variable! The reason for this is that a time average of a stochastic process is simply the average value of a single outcome of a stochastic process. Note that this means that unlike the ensemble average, the time average is not a function, but a value (a number). It can be described mathematically as:

$$ \lim_{T\to\infty} \frac{1}{2T}\int_{-T}^T X(t)dt $$

where $X(t)$ is the stochastic process in question, evaluated at time $t$.

To wrap up: ensemble and time averages are properties of stochastic processes, which are like random variables but take the form of waveforms. Ensemble average is analogous to expected value or mean, in that it represents a sort of "average" for the stochastic process. It is a function of the same variable as the stochastic process, and when evaluated at a particular value denotes the average value that the waveforms will have at that same value. Time average is more like a typical average, in that it is the average value of a single outcome of a stochastic process. It is a random variable itself, as it depends upon which outcome it is being evaluated for (and the outcome itself is random).