There are often many routes to solving a given data analysis challenge. For example, in a time-series classification problem, the two classes may be perfectly distinguished based on their lag-1 autocorrelation, and also on their Lyapunov exponent spectrum, and also on hundreds of other properties. In general, one should avoid interpreting the most complex features (like Lyapunov exponents) as being uniquely useful for a problem, as they reproduce the behavior of much simpler features, which provide a more interpretable and parsimonious interpretation of the relevant patterns in the dataset. For other problems, time-series analysis methods (that are sensitive to the time-ordering of the data samples) may not provide any benefit at all over properties of the data distribution (e.g., the variance), or more trivial differences in time-series length across classes.