Robust Learning of Noisy Time Series Collections Using Stochastic Process Models with Motion Codes

arXiv:2402.14081v1 Announce Type: new
Abstract: While time series classification and forecasting problems have been extensively studied, the cases of noisy time series data with arbitrary time sequence lengths have remained challenging. Each time series instance can be thought of as a sample realization of a noisy dynamical model, which is characterized by a continuous stochastic process. For many applications, the data are mixed and consist of several types of noisy time series sequences modeled by multiple stochastic processes, making the …

abstract arxiv cases classification cs.ai cs.lg data forecasting instance process robust sample series stat.ml stochastic stochastic process thought time series type