DeepAI AI Chat
Log In Sign Up

Modeling Regime Shifts in Multiple Time Series

by   Etienne Gael Tajeuna, et al.
Université de Sherbrooke

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 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.


page 22

page 26

page 27


Threshold factor models for high-dimensional time series

We consider a threshold factor model for high-dimensional time series in...

Robust Monitoring of Time Series with Application to Fraud Detection

Time series often contain outliers and level shifts or structural change...

Time-Discounting Convolution for Event Sequences with Ambiguous Timestamps

This paper proposes a method for modeling event sequences with ambiguous...

Forecasting with Multiple Seasonality

An emerging number of modern applications involve forecasting time serie...

Forecasting Sleep Apnea with Dynamic Network Models

Dynamic network models (DNMs) are belief networks for temporal reasoning...

Changepoint Detection: An Analysis of the Central England Temperature Series

This paper presents a statistical analysis of structural changes in the ...

Sampling rate-corrected analysis of irregularly sampled time series

The analysis of irregularly sampled time series remains a challenging ta...