Two thoughts off the top of my head. The first is that I often see DH beginners trying to make continuous or ordinal data work as categorical data. I don’t think it’s impossible to do this, but you’re often trying to make a square peg fit into a round hole. In a classification task, for example, a text from 1925 fitted to the 1900-1924 group is just in the wrong category rather than being scored as close to correct. (I’ve seen people put gaps in the date ranges to avoid this issue, e.g. classifying 1900-1920, then jumping to 1925-1945, but I don’t love this approach.) In my work, such as this ACH paper, I treated time as a continuous variable (linear regression) and good results, but I had data for every year between 1880 and 1925. The advantage of treating time as a continuous variable is that every float value has meaning. 1900.5 is modeled as a value halfway between 1900 and 1901. Further, the distance between any two variables is known, so a model that predicts a value of 1898 for ground truth of 1901 is off by 3 years. This allows you to calculate the average absolute error rate, which can be useful. My second thought is that, yes, there should be cases when the data require treating time as ordinal, especially if we have a fuzzier sense of a timeline or if you’re more interested in order than exact years. One example that comes to mind is modeling authorial periods when conducting stylochronometry, as in van Hulle, Dirk, and Mike Kestemont. “Periodizing Samuel Beckett’s Works: A Stylochronometric Approach.” Style 50, no. 2 (2016): 172-202. doi:10.1353/sty.2016.0003. If memory serves, they use an unsupervised clustering approach that weighed whether texts were directly before or after one another. @mike.kestemont I hope I’m not butchering this summary; feel free to correct me or add any detail I should have included.