Regime switching volatility models provide a tractable method of modelling stochastic volatility. Currently the most popular method of regime switching calibration is the Hamilton filter. We propose using the Baum-Welch algorithm, an established technique from Engineering, to calibrate regime switching models instead. …
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18 results for “baum-welch”
The Hidden Markov Model (HMM) is one of the mainstays of statistical modeling of discrete time series, with applications including speech recognition, computational biology, computer vision and econometrics. Estimating an HMM from its observation process is often addressed via the Baum-Welch algorithm, which is known t…
New algorithm processes Riemannian data more efficiently.
problem High memory usage and slow speed in previous Riemannian HMMs.
method Online algorithm based on Baum-Welch adapted for Riemannian manifolds.
result Significant improvements in speed and efficiency.
Novel online algorithm for hierarchical imitation learning.
problem Scalability issue in reinforcement learning and options discovery.
method Online Baum-Welch algorithm for hierarchical imitation learning.
result The online algorithm outperforms the batch version in both discrete and continuous environments.
Machine learning provides algorithms that can learn from data and make inferences or predictions on data. Stochastic acceptors or probabilistic automata are stochastic automata without output that can model components in machine learning scenarios. In this paper, we provide dynamic programming algorithms for the comput…
As one of Bayesian analysis tools, Hidden Markov Model (HMM) has been used to in extensive applications. Most HMMs are solved by Baum-Welch algorithm (BWHMM) to predict the model parameters, which is difficult to find global optimal solutions. This paper proposes an optimized Hidden Markov Model with Particle Swarm Opt…
Hidden Quantum Markov Models (HQMMs) can be thought of as quantum probabilistic graphical models that can model sequential data. We extend previous work on HQMMs with three contributions: (1) we show how classical hidden Markov models (HMMs) can be simulated on a quantum circuit, (2) we reformulate HQMMs by relaxing th…
This paper describes a new method, HMM gauge likelihood analysis, or GLA, of detecting anomalies in discrete time series using Hidden Markov Models and clustering. At the center of the method lies the comparison of subsequences. To achieve this, they first get assigned to their Hidden Markov Models using the Baum-Welch…
Markov Observation Modelsstat.ML
Expands Hidden Markov Model to include Markov chain observations.
problem Handling Markov chain observations in Hidden Markov Models.
method Developed Expectation-Maximization algorithm and Viterbi algorithm analogs.
result Estimates transition probabilities for hidden states and observations.
Hidden Markov Models (HMM) have been used for several years in many time series analysis or pattern recognitions tasks. HMM are often trained by means of the Baum-Welch algorithm which can be seen as a special variant of an expectation maximization (EM) algorithm. Second-order training techniques such as Variational Ba…
DenseHMM improves HMMs by learning dense representations that enable gradient-based optimization.
problem Learning dense representations for hidden states and observables in HMMs.
method DenseHMM uses kernelized transition probabilities and two optimization schemes.
result DenseHMM achieves superior performance and expressiveness compared to standard HMMs.
This dissertation shows that careful injection of noise into sample data can substantially speed up Expectation-Maximization algorithms. Expectation-Maximization algorithms are a class of iterative algorithms for extracting maximum likelihood estimates from corrupted or incomplete data. The convergence speed-up is an e…
A Semi-Hidden Markov Model (SHMM) for bursty error channels is defined by a state transition probability matrix , a prior probability vector , and the state dependent output symbol error probability matrix . Several processes are utilized for estimating , and from a given empirically obtained or sim…
We propose a novel iterative channel estimation (ICE) algorithm that essentially removes the critical known noisy channel assumption for universal discrete denoising problem. Our algorithm is based on Neural DUDE (N-DUDE), a recently proposed neural network-based discrete denoiser, and it estimates the channel transiti…
Time series are used in many domains including finance, engineering, economics and bioinformatics generally to represent the change of a measurement over time. Modeling techniques may then be used to give a synthetic representation of such data. A new approach for time series modeling is proposed in this paper. It cons…
Paper improves spectral learning of HMMs to avoid local optima and improve robustness.
problem Spectral learning of HMMs can get stuck in local optima and degrade due to unchecked error propagation.
method Developed a novel algorithm (PSHMM) and online learning variants to mitigate error propagation and nonstationarity.
result PSHMM provides more robust estimation and forecasting compared to SHMM and B-W algorithm.
Hybrid model improves synthetic equity data generation.
problem Generating realistic synthetic financial time series.
method Discretized excess growth rates into states with Poisson jumps, estimating parameters directly.
result Framework achieved high pass rates for distributional and volatility clustering tests.
A HMM for intraday momentum trading reduces lagging and incorporates side information.
problem Time-lagging in existing momentum trading models leads to incorrect momentum signals.
method State space formulation with latent momentum states, cross-validation for state estimation, and Bayesian inference for prediction.
result The model reduces lagging and accurately predicts market changes.