Estimating an Activity Driven Hidden Markov Model

Abstract

We define a Hidden Markov Model (HMM) in which each hidden state has time-dependent $\text{activity levels}$ that drive transitions and emissions, and show how to estimate its parameters. Our construction is motivated by the problem of inferring human mobility on sub-daily time scales from, for example, mobile phone records.

13 pages, 2 figures

References in corpus (2)

• Understanding individual human mobility patterns
• Exploring the Mobility of Mobile Phone Users