After taking a course on SDEs I have started studying time series on my own. However, I am having difficulties in drawing parallelisms between the two subjects.
I have the following definition of $L^p$ convergence in my time series book. For the sake of simplicity take $T_n = \{t\in\mathbb{Z}: \lvert t \rvert \leq n\}$.
Definition: A series $\sum_t X_t$ converges in pth mean if there is a random variable $Y$ such that $Y_n := \sum_{t\in T_n}X_t \rightarrow Y$ in pth mean, i.e. $E[\lvert Y_n - Y\rvert^p] \rightarrow 0$. The limit is then denoted $\sum_t X_t$.
Here comes the part where I am having difficulties.
Lemma: Let $(X_t: t\in\mathbb{Z})$ be an arbitrary countable set of random variables. If $\sum_t E[\lvert X_t\rvert] < \infty$, then the series $\sum_t X_t$ converges absolutely almost surely and in mean. Furthermore, $E[\sum_t X_t] = \sum_tE[X_t]$.
My first question is about the term "converges absolutely almost surely". I am not sure what this means. I know absolute convergence for an ordinary series and almost sure convergence for a sequence of random variables but absolute almost sure convergence is new to me. If I had to guess I would formulate this concept as follows. We say $\sum_t X_t$ converges absolutely almost surely if there exists a random variable $Y$ such that $Y_n := \sum_{t\in T_n}\lvert X_t\rvert \rightarrow Y$ almost surely and $Y < \infty$ almost surely. (I could put these two conditions under one "almost surely" but that won't make a difference.) If that is the case, then the proof of the first claim in the lemma simply follows from the monotone convergence theorem.
$$E\left[\sum_t \lvert X_t\rvert\right] = \sum_t E[\lvert X_t\rvert] < \infty$$ This gives $\sum_t \lvert X_t\rvert < \infty$ almost surely. Can someone correct the mistakes/fill the gaps in my reasoning and/or understanding?
My second question is about showing convergence in mean, i.e. whether $E[\lvert \sum_{t\in T_n} X_t - Z\rvert]\rightarrow 0$ where $Z = \sum_{t\in \mathbb{Z}} X_t$. $$E[\lvert \sum_{t\in T_n} X_t - Z\rvert] = E[\lvert \sum_{t\in Z\setminus T_n} X_t \rvert] \leq E[ \sum_{t\in Z\setminus T_n} \lvert X_t \rvert] = \sum_{t\in Z\setminus T_n} E[\lvert X_t \rvert]$$
The RHS are the right tails of the sum $\sum_{t\in Z} E[\lvert X_t \rvert]$, which is given to be finite. So the tails must converge to $0$. Is this correct?