A univariate time series is where the measurements are collected over a standard measure of time, which could be by the minute, hour, day, week, or month. What makes the time series problematic over the other data collected is that the order of the observations probably matters. This dependency of order can cause the standard analysis methods to produce an unnecessarily high bias or variance.

It seems that there is a paucity of literature on machine learning and time series data. This is unfortunate as so much of the real-world data involves a time component. Furthermore, time series analysis can be quite complicated and tricky. I would say that if you haven't seen a time series analysis done incorrectly, you haven't been looking close enough.

Another aspect involving time series that is often neglected is causality. Yes, we...