A Problem of Prediction with Decision Tree We know, roughly speaking, the algorithm of decision tree is like an approximation with piecewise constants.
For example, suppose we have a function $f$ from $[0,1]$ to $\mathbb R$. Then we can use decision tree to approximate it, i.e. using piecewise constant functions to approximate it. The trained model can tell us an output for any given input $a\in [0,1]$. But it cannot predict an output for an input $b\in [2,3]$.
Now, let us take the $x$-axis as time and the $y$-axis as stock price. Suppose the time interval is $[1,100]$, where every integer means one day. Then by doing decision tree, we will have a model. But this model can only be used to predict the stock price of any time in $[1,100]$, not for the future. That's, we cannot use it to predict the stock price at time $101$, which is outside $[1,100]$.
But there are people using decision tree to predict the stock prices. Why can they make sense of the reasoning? [I tried to read their sample codes but without clear comments along with them I couldn't really understand the logics.]
Thanks for helping me make sense of the logics.
 A: As also pointed out in the comments section, time is usually not in your feature list when you do time series prediction with tree-based algorithms.
Typical features are target values at previous time instants, other hand crafted features like  weekend, evening indicators etc.
A: More generally (ignoring the specifics of the problem), decision trees (and related algorithms like gradient boosted decision trees, random forest etc.) will make predictions for numeric feature values outside of the range of feature values seen at training. The splits for branches of the tree are usually done as "variable #1 < X" vs. "variable #1 >= X" (whether it's < and >=, or <= and > doesn't really matter) and such splits are only done at values that lie within the range of values seen during training, so any value higher than values seen during training will essentially be treated like the highest value seen during training. Similarly, the a value lower than the lowest value seen during training will be treated like the lowest value seen during training.
In other words, no matter how far outside the range of values seen during training, the predictions will no longer change beyond the highest (or lowest) feature value seen during training.
This has also been summarized as "decision trees don't extrapolate", which is to say that even if there is e.g. a blatantly obvious linear relationship of feature to outcome, a decision tree will not continue to extrapolate it, but rather "stay flat with its predictions".
In contrast, other models (e.g. linear regression, neural networks etc.) will "happily" extrapolate patterns beyond the training data. Of course, this may often be a very, very wrong thing to do.
In general, extrapolation beyond the training data involves a lot of assumptions and is usually best done if one truly understands the underlying data generating process  instead of modeling it empirically. An example where it is to some extent possible is e.g. predicting drug concentrations for individuals that are much larger or smaller than those in early clinical trials of a drug from pharmacokinetic model (that tends to model the process of absorption, distribution and excretion of a drug taking into account some characteristics of patients). If one does not have such a situation, then perhaps we sometimes need to predict something, but should then be much more cautious about relying on the prediction.
