  Within the context of multivariate time series segmentation this paper
proposes a method inspired by a posteriori optimal trading. After a
normalization step time series are treated channel-wise as surrogate stock
prices that can be traded optimally a posteriori in a virtual portfolio holding
either stock or cash. Linear transaction costs are interpreted as
hyperparameters for noise filtering. Resulting trading signals as well as
resulting trading signals obtained on the reversed time series are used for
unsupervised labeling, before a consensus over channels is reached that
determines segmentation time instants. The method is model-free such that no
model prescriptions for segments are made. Benefits of proposed approach
include simplicity, adaptability to a wide range of different shapes of time
series, and in particular computational efficiency that make it suitable for
big data. Performance is demonstrated on synthetic and real-world data,
including a large-scale dataset comprising a multivariate time series of
dimension 1000 and length 2709. Proposed method is compared to a popular
model-based bottom-up approach fitting piecewise affine models and to a
state-of-the-art model-based top-down approach fitting Gaussian models, and
found to be consistently faster while producing more intuitive results.
