Thanks for the thoughts and the pointers to articles.
You touch upon some of the issues I was referring too. Let’s see if we can dive a bit deeper into these issues. I’m not so much talking about classification tasks, in which I think binning time is indeed problematic, although sometimes necessary.
One of the difficulties in working with time is that people’s lived experience in a period is based on their perceptions of the past and future. This makes the perception of time more condensed in some periods than in others. This ties into the treating time as a continuous variable, while I see that when approaching time as a continuous variable, every float has a meaning, it’s not necessarily the case that the meaning is the same for the same distances between floats.
I often work with data that is fuzzy, has measurement errors, or is just missing. In those cases, ordinals can help, especially when modeling. Still, it feels weird treating time as such, since it’s not only the order but also the structure between the intervals. Happy to hear about possible ways to estimate these measurement errors for time stamps.
In a current project, I approach time using multilevel analysis. I treat datapoints referring to time on a continuous scale while using varying intercepts for clusters that make sense (years, decades, certain regimes, etc.). In this case, we can (hopefully) explain some of the variance and lack of data in some periods away through such a grouping.
Another approach that I have applied with @knielbo is Adaptive Fractal Analysis. This algorithm adaptively detrends to the time series to extract smoothed trends and then extracts different scaling regimes. In a way, these scaling regimes express the memory function, which we contend might be used as a proxy to study ‘lived experience/cultural memory’. The paper should be published any time soon now, here’s an ArXiv link).