@inproceedings{c95e95dec8364d7496fff75fb235f90e,
title = "Sequential learning of Multi-state autoregressive time series",
abstract = "Modeling and forecasting streaming data has fundamental importance in many real world applications. In this paper, we present an online model selection technique that can be used to model non-stationary time series in a sequential manner. Multi-state autoregressive (AR) model is used to describe non-stationary time series, and a dynamic algorithm is applied to learn the states at each time step. The proposed technique estimates a candidate AR filter from the most recent data points at every time step, and checks whether starting a new state significantly decreases prediction error or not. To that end, a time-varying threshold is compared with the reduction in the prediction error caused by postulating a new AR filter. The threshold is calculated by sampling and clustering uniformly distributed stable AR filters. Numerical simulations show that the proposed algorithm accurately estimates the state transitions with a small delay.",
keywords = "Autoregressive process, Multi-state process, Online update, Sequential learning",
author = "Mohammad Noshad and Jie Ding and Vahid Tarokh",
year = "2015",
month = oct,
day = "9",
doi = "10.1145/2811411.2813523",
language = "English (US)",
series = "Proceeding of the 2015 Research in Adaptive and Convergent Systems, RACS 2015",
publisher = "Association for Computing Machinery, Inc",
pages = "44--51",
booktitle = "Proceeding of the 2015 Research in Adaptive and Convergent Systems, RACS 2015",
note = "Research in Adaptive and Convergent Systems, RACS 2015 ; Conference date: 09-10-2015 Through 12-10-2015",
}