Our timeseries are already in a single length (500). However, their values are usually in various ranges. This is not ideal for a neural network; in general we should seek to make the input values normalized. For this specific dataset, the data is already z-normalized: each timeseries sample has a mean equal to zero and a standard deviation equal to one. This type of normalization is very common for timeseries classification problems, see Bagnall et al. (2016).