This paper addresses the problem of change-point detection in sequences high-dimensional and heterogeneous observations, which also possess a periodic temporal structure. Due to dimensionality problem, when time between change points is order dimension model parameters, drifts underlying distribution can be misidentified as changes. To overcome this limitation, we assume that observations lie l...

We consider a function estimation method with change point detection using truncated power spline basis and elastic-net-type L1-norm penalty. The penalty controls the jump smoothness depending on value of parameter. In terms proposed estimators, we introduce two computational algorithms for Lagrangian dual problem (coordinate descent algorithm) constrained convex optimization (an algorithm base...

Abstract It is quite common that the structure of a time series changes abruptly. Identifying these change points and describing model in segments between an important task financial analysis. Change point detection identification abrupt generative parameters sequential data. In application areas such as finance, online rather than offline mostly required, due to their use predictive tasks, pos...