Unified Treatment of Hidden Markov Switching Models

04/11/2011
by   Silvia Chiappa, et al.
0

Many real-world problems encountered in several disciplines deal with the modeling of time-series containing different underlying dynamical regimes, for which probabilistic approaches are very often employed. In this paper we describe several such approaches in the common framework of graphical models. We give a unified overview of models previously introduced in the literature, which is simpler and more comprehensive than previous descriptions and enables us to highlight commonalities and differences among models that were not observed in the past. In addition, we present several new models and inference routines, which are naturally derived within this unified viewpoint.

READ FULL TEXT
research
04/29/2015

Market forecasting using Hidden Markov Models

Working on the daily closing prices and logreturns, in this paper we dea...
research
04/30/2020

A primer on coupled state-switching models for multiple interacting time series

State-switching models such as hidden Markov models or Markov-switching ...
research
09/17/2019

sktime: A Unified Interface for Machine Learning with Time Series

We present sktime -- a new scikit-learn compatible Python library with a...
research
08/17/2022

Expressivity of Hidden Markov Chains vs. Recurrent Neural Networks from a system theoretic viewpoint

Hidden Markov Chains (HMC) and Recurrent Neural Networks (RNN) are two w...
research
09/12/2019

Explicit-Duration Markov Switching Models

Markov switching models (MSMs) are probabilistic models that employ mult...
research
02/02/2021

Time Adaptive Gaussian Model

Multivariate time series analysis is becoming an integral part of data a...

Please sign up or login with your details

Forgot password? Click here to reset