Modeling Regime Shifts in Multiple Time Series
- URL: http://arxiv.org/abs/2109.09692v1
- Date: Mon, 20 Sep 2021 17:02:29 GMT
- Title: Modeling Regime Shifts in Multiple Time Series
- Authors: Etienne Gael Tajeuna and Mohamed Bouguessa and Shengrui Wang
- Abstract summary: Regime shifts refer to the changing behaviors exhibited by series at different time intervals.
Existing methods fail to take relationships between time series into consideration for discovering regimes in multiple time series.
Most of the existing methods are unable to handle all of these three issues in a unified framework.
- Score: 1.4588552933974936
- License: http://creativecommons.org/publicdomain/zero/1.0/
- Abstract: We investigate the problem of discovering and modeling regime shifts in an
ecosystem comprising multiple time series known as co-evolving time series.
Regime shifts refer to the changing behaviors exhibited by series at different
time intervals. Learning these changing behaviors is a key step toward time
series forecasting. While advances have been made, existing methods suffer from
one or more of the following shortcomings: (1) failure to take relationships
between time series into consideration for discovering regimes in multiple time
series; (2) lack of an effective approach that models time-dependent behaviors
exhibited by series; (3) difficulties in handling data discontinuities which
may be informative. Most of the existing methods are unable to handle all of
these three issues in a unified framework. This, therefore, motivates our
effort to devise a principled approach for modeling interactions and
time-dependency in co-evolving time series. Specifically, we model an ecosystem
of multiple time series by summarizing the heavy ensemble of time series into a
lighter and more meaningful structure called a \textit{mapping grid}. By using
the mapping grid, our model first learns time series behavioral dependencies
through a dynamic network representation, then learns the regime transition
mechanism via a full time-dependent Cox regression model. The originality of
our approach lies in modeling interactions between time series in regime
identification and in modeling time-dependent regime transition probabilities,
usually assumed to be static in existing work.
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