That's because it's a one-step behind prediction. (If you go to the linked notebook and look carefully at the graph its evident.) If you could shift the prediction one step forward, you'd be rich!

It's because the test dataset involves randomly choosing samples spread throughout the time series.

Imagine dropping 10% of the pixels randomly spaced out in a historical graph and then try to fill them in. It's trivial because you can just average the previous and next sample and have an extremely high accuracy.

for one, the author scaled their mean-OLHC values _prior_ to making their train-test split, meaning data from the test set has leaked into the training set. Time series prediction is a bit easier if you understand how the future values are distributed!

Ditto. There’s no way this is projecting almost a year into the future and maintaining accuracy. It’s being trained on the test somehow or it’s doing something different from what I’m interpreting.