Why is performance worse when my time-series data is not shuffled prior to a train/test split vs. when it is shuffled prior to the split?
Without looking further into the data myself, I can surmise that something has changed recently with your data such that if you split without shuffling, some aspect of the data in your test set (which is what you most recently collected) is underrepresented in your training set. By shuffling the data, you allow those more recent samples to also be present in your training set, and thus your test set performance improves.