13,141 research outputs found
Pattern Recognition for Command and Control Data Systems
To analyze real-world events, researchers collect observation data from an underlying process and construct models to represent the observed situation. In this work, we consider issues that affect the construction and usage of a specific type of model. Markov models are commonly used because their combination of discrete states and stochastic transitions is suited to applications with both deterministic and stochastic components. Hidden Markov Models (HMMs) are a class of Markov model commonly used in pattern recognition. We first demonstrate how to construct HMMs using only the observation data, and no a priori information, by extending a previously developed approach from J.P. Crutchfield and C.R. Shalizi. We also show how to determine with a level of statistical confidence whether or not the model fully encapsulates the underlying process. Once models are constructed from observation data, the models are used to identify other types of observations. Traditional approaches consider the maximum likelihood that the model matches the observation, solving a classification problem. We present a new method using confidence intervals and receiver operating characteristic curves. Our method solves a detection problem by determining if observation data matches zero, one, or more than one model. To detect the occurrence of a behavior in observation data, one must consider the amount of data required. We consider behaviors to be \u27serial Markovian,\u27 when the behavior can change from one model to another at any time. When analyzing observation data, considering too much data induces high delay and could lead to confusion in the system if multiple behaviors are observed in the data stream. If too little data is used, the system has a high false positive rate and is unable to correctly detect behaviors. We demonstrate the effectiveness of all methods using illustrative examples and consumer behavior data
Using Markov Models and Statistics to Learn, Extract, Fuse, and Detect Patterns in Raw Data
Many systems are partially stochastic in nature. We have derived data driven
approaches for extracting stochastic state machines (Markov models) directly
from observed data. This chapter provides an overview of our approach with
numerous practical applications. We have used this approach for inferring
shipping patterns, exploiting computer system side-channel information, and
detecting botnet activities. For contrast, we include a related data-driven
statistical inferencing approach that detects and localizes radiation sources.Comment: Accepted by 2017 International Symposium on Sensor Networks, Systems
and Securit
An early warning indicator for atmospheric blocking events using transfer operators
The existence of persistent midlatitude atmospheric flow regimes with
time-scales larger than 5-10 days and indications of preferred transitions
between them motivates to develop early warning indicators for such regime
transitions. In this paper, we use a hemispheric barotropic model together with
estimates of transfer operators on a reduced phase space to develop an early
warning indicator of the zonal to blocked flow transition in this model. It is
shown that, the spectrum of the transfer operators can be used to study the
slow dynamics of the flow as well as the non-Markovian character of the
reduction. The slowest motions are thereby found to have time scales of three
to six weeks and to be associated with meta-stable regimes (and their
transitions) which can be detected as almost-invariant sets of the transfer
operator. From the energy budget of the model, we are able to explain the
meta-stability of the regimes and the existence of preferred transition paths.
Even though the model is highly simplified, the skill of the early warning
indicator is promising, suggesting that the transfer operator approach can be
used in parallel to an operational deterministic model for stochastic
prediction or to assess forecast uncertainty
Statistical identification with hidden Markov models of large order splitting strategies in an equity market
Large trades in a financial market are usually split into smaller parts and
traded incrementally over extended periods of time. We address these large
trades as hidden orders. In order to identify and characterize hidden orders we
fit hidden Markov models to the time series of the sign of the tick by tick
inventory variation of market members of the Spanish Stock Exchange. Our
methodology probabilistically detects trading sequences, which are
characterized by a net majority of buy or sell transactions. We interpret these
patches of sequential buying or selling transactions as proxies of the traded
hidden orders. We find that the time, volume and number of transactions size
distributions of these patches are fat tailed. Long patches are characterized
by a high fraction of market orders and a low participation rate, while short
patches have a large fraction of limit orders and a high participation rate. We
observe the existence of a buy-sell asymmetry in the number, average length,
average fraction of market orders and average participation rate of the
detected patches. The detected asymmetry is clearly depending on the local
market trend. We also compare the hidden Markov models patches with those
obtained with the segmentation method used in Vaglica {\it et al.} (2008) and
we conclude that the former ones can be interpreted as a partition of the
latter ones.Comment: 26 pages, 12 figure
Experiment-friendly kinetic analysis of single molecule data in and out of equilibrium
We present a simple and robust technique to extract kinetic rate models and
thermodynamic quantities from single molecule time traces. SMACKS (Single
Molecule Analysis of Complex Kinetic Sequences) is a maximum likelihood
approach that works equally well for long trajectories as for a set of short
ones. It resolves all statistically relevant rates and also their
uncertainties. This is achieved by optimizing one global kinetic model based on
the complete dataset, while allowing for experimental variations between
individual trajectories. In particular, neither a priori models nor equilibrium
have to be assumed. The power of SMACKS is demonstrated on the kinetics of the
multi-domain protein Hsp90 measured by smFRET (single molecule F\"orster
resonance energy transfer). Experiments in and out of equilibrium are analyzed
and compared to simulations, shedding new light on the role of Hsp90's ATPase
function. SMACKS pushes the boundaries of single molecule kinetics far beyond
current methods.Comment: 11 pages, 8 figure
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