I am currently studying Markov models from the textbook Introduction to Modeling and Analysis of Stochastic Systems, second edition, by Kulkarni. I have just encountered the concept of periodicity in chapter 2:
Definition 2.4. (Periodicity). Let $\{ X_n, n \ge 0 \}$ be an irreducible DTMC on state space $S = \{1, 2, \dots, N \}$, and let $d$ be the largest integer such that
$$P(X_n = i \vert X_0 = i) > 0 \Rightarrow n \ \text{is an integer multiple of} \ d \tag{2.48}$$
for all $i \in S$. The DTMC is said to be periodic with period $d$ if $d > 1$ and aperiodic if $d = 1$.
So let's say we have a Markov chain with transition matrix $P$, and we want to find the period of each state. We then calculate $P^2, P^3, P^4, P^5, P^6$. It is found that $p_{ii} = p^3_{ii} = p^5_{ii} = 0$, and $p^2_{ii} \not= p^4_{ii} \not= p^6_{ii}$ and $p^2_{ii} > 0, p^4_{ii} > 0, p^6_{ii} > 0$. What can we conclude here? For the $n = 1, 3, 5$ cases, we would have that $d = 1$, right? But for the $n = 2, 4, 6$ case, I guess we would have that $d = 2$, right? So is this periodicity, or is it aperiodicity? And what specifically is meant by period of each state?
I would greatly appreciate it if people would please take the time to explain this.
An equivalent definition is:
$ d = gcd(n>0 : Pr(X_n = i | X_0 = i) >0) $
Gcd stands for the greatest common divisor.
If we want to apply this definition to your specific example, the answer would be either 1 or 2. If you could, further, prove that there are no odd length paths, the answer would be 2. If you could prove the opposite the answer would be 1. However, you can not claim anything before proving what the values for larger paths are.
Essentially, if the state is periodic, you can go around the path and get the to initial states only in multiple of period number of steps. The periodicity is very interesting property as all the states with the same period form an equivalence class of states that communicate. Furthermore, all the states within the class are either transient or recurrent.
I am not an expert on Markov chains, but I hope this helps!