Color your vector of numbers in such way that red color marks $\color{red}{\text{higher}}$ and blue color marks $\color{blue}{\text{lower}}$ number than the preceding one:
$$
\color{black}{46},
\color{red}{73},
\color{blue}{29},
\color{blue}{12},
\color{blue}{4},
\color{red}{27},
\color{red}{28},
\color{red}{81},
\color{blue}{62}
$$
first number is left black as we do not know it's history. Now count the changes in pairs of numbers: how often red precedes blue, red precedes red, blue precedes red, blue precedes red.
$$
\begin{array}{cc}
& \color{blue}{\text{lower}} & \color{red}{\text{higher}} \\
\color{blue}{\text{lower}} & 2 & 1 \\
\color{red}{\text{higher}} & 2 & 2 \\
\end{array}
$$
Now you know that given your data, conditional probabilities are easy to calculate, for example
$$\Pr(X_i = \color{red}{\text{higher}} ~|~ X_{i-1} = \color{blue}{\text{lower}}) = 1/3$$
since there were three cases when previous value was lower and only in one case it was followed by a change to higher value.
This gives you the exact answer to the question about probability of increase or decrease given the previous value in the series. Unfortunately, it discards whole a lot of information in the data (the actual values). If you need something more complicated you should look at time series forecasting models. The same, if you have any background knowledge about the process that generated the data, you could use it to build more precise model of the series behavior. However if you know nothing about the numbers and the only thing that you have are very short vectors of numbers (as in your example), than you probably don't have enough data to build more complicated model than something like the approach described above.
Tim - based on the set of numbers given above.
– Alvin Feb 11 '16 at 17:42