Comparing Prior and Learned Time Representations in Transformer Models of Timeseries

What sets timeseries analysis apart from other machine learning To elaborate on the various considerations that need to be addressed, exercises is that time representation becomes a primary aspect of first consider that one cannot assume fully observed, uniformly the experiment setup, as it must adequately represent the temporal sampled inputs as there might be gaps in the data, varying relations that are relevant for the application at hand. In the work sampling rates, and (for multivariate timeseries) misalignment described here we study wo different variations of the Transformer between the time steps of the different variables. This dictates a architecture: one where we use the fixed time representation proposed representation that allows time differences to be computed, so that in the literature and one where the time representation is (for example) September 2023 is'closer' to January 2024 than it is to learned from the data. Our experiments use data from predicting September 2022. Simple timestamps allow this but do not capture the energy output of solar panels, a task that exhibits known periodicities periodicity: Consider, for instance, an application with seasonal (daily and seasonal) that is straight-forward to encode in periodicity where September 2023 is'closer' to September 2022 the fixed time representation. Our results indicate that even in an than to January 2024.