I am trying to learn how to use Neural Networks. I was reading this tutorial.
After fitting a Neural Network on a Time Series using the value at $t$ to predict the value at $t+1$ the author obtains the following plot, where the blue line is the time series, the green is the prediction on train data, red is the prediction on test data (he used a test-train split)
and calls it "We can see that the model did a pretty poor job of fitting both the training and the test datasets. It basically predicted the same input value as the output."
Then the author decides to use $t$, $t-1$ and $t-2$ to predict the value at $t+1$. In doing so obtains
and says "Looking at the graph, we can see more structure in the predictions."
Why is the first "poor"? it looks almost perfect to me, it predicts every single change perfectly!
And similarly, why is the second better? Where is the "structure"? To me it seem much poorer than the first one.
In general, when is a prediction on time series good and when is it bad?