I have a set of states $A = ${$ a_0, a_1, ..., a_n $}. A model predicts $y$, an ordered list in which these states were observed over time, e.g. $y = (a_3, a_1, a_4)$. Not every $a_i$ in $A$ needs to be observed, so $|y|$ is not necessarily $|A|$.
Now I am getting stuck on how to declare $y$ clearly.
I thought about $y = (a_i, a_{i+1}, ..., a_m), a\in A$, but I feel like this notation forces $a_3$ to be after $a_2$, which should really not be the case. But the notation should show that $y$ is ordered based on the times at which the states were observed, in this case $a_i$ being the first and $a_m$ being the last.
Any help on this would greatly be appreciated. I hope the question is clear, otherwise I am happy to clarify.
One way is to introduce a function $w$ that iterates over the desired / observed indices:
$$w:\{1,2,...,m\}\to \{1,2,...,n\}$$
so that the $m$-tuple of observed states is
$$y = (a_{w(1)}, a_{w(2)}, ..., a_{w(m)})$$
$w$ need not be surjective because not every state will occur in $y$, but it must be injective because no state will occur more than once: $$i\neq j\implies w(i)\neq w(j)$$
On notation: For functions on discrete domain, indexed notation like $w_1$ etc. is not uncommon. But as $w$ itself appears as indices, this might have negative impact on legibility: $y = (a_{w_1}, a_{w_2}, ..., a_{w_m})$. Therefore I used notation $w(i)$.