On the clustering behavior of sliding windows
This addresses potential pitfalls in time series analysis for researchers and practitioners, though it appears incremental as it focuses on explaining known issues rather than proposing new solutions.
The paper identified three surprising failure modes that occur when clustering time series data preprocessed with sliding windows, depending on window size relative to time series length, and provided theoretical explanations for these failures.
Things can go spectacularly wrong when clustering timeseries data that has been preprocessed with a sliding window. We highlight three surprising failures that emerge depending on how the window size compares with the timeseries length. In addition to computational examples, we present theoretical explanations for each of these failure modes.