Longitudinal Support Vector Machines for High Dimensional Time Series
This work addresses the challenge of binary classification for long-term multivariate time series, which is incremental as it adapts an existing method to a specific data type.
The authors tackled the problem of classifying high-dimensional time series data by extending the margin concept of support vector machines to continuous functional data, resulting in a convex optimization algorithm with empirical efficacy demonstrated through significance tests.
We consider the problem of learning a classifier from observed functional data. Here, each data-point takes the form of a single time-series and contains numerous features. Assuming that each such series comes with a binary label, the problem of learning to predict the label of a new coming time-series is considered. Hereto, the notion of {\em margin} underlying the classical support vector machine is extended to the continuous version for such data. The longitudinal support vector machine is also a convex optimization problem and its dual form is derived as well. Empirical results for specified cases with significance tests indicate the efficacy of this innovative algorithm for analyzing such long-term multivariate data.