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arXiv research

A locally-built, LLM-digested index of recent arXiv papers in quant finance, geometry/topology, and statistical ML — keyword search served straight from SQLite on this machine.

168,922 papers · 148 categories

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85170254339 · Jun 202019922001200920172026
48 results for switched systems

This work extends identifiability analysis to sequential latent variable models, focusing on Switching Dynamical Systems.

problem Identifying latent variables in sequential data models.
method Proved identifiability of Markov Switching Models and established conditions for Switching Dynamical Systems.
result Identifiability of latent variables and non-linear mappings in Switching Dynamical Systems up to affine transformations.

Optimizes control of hybrid systems with multiple switching processes.

problem Optimal control of hybrid systems with multiple Markov switching processes.
method Combines two separate Markov chains into one synthetic chain, derives HJB equations, and solves the portfolio choice problem.
result Derives explicit solutions and value functions for the optimal control problem.

The paper provides guarantees for learning switching non-linear systems from a single trajectory.

problem Learning non-linear dynamical systems with switching dynamics.
method Non-asymptotic bounds derived under stability assumptions for i.i.d. switching modes.
result Explicit convergence rates for Hölder and linear function classes based on effective sample size.

LaMBO optimizes modular systems with switching costs, achieving better results than existing methods.

problem Optimizing systems with costly variable updates in a sequence of modules.
method Lazy Modular Bayesian Optimization (LaMBO) that minimizes switching costs.
result LaMBO achieves vanishing regret and improves over existing cost-aware Bayesian optimization algorithms.

Code-switching, the alternation of languages within a conversation or utterance, is a common communicative phenomenon that occurs in multilingual communities across the world. This survey reviews computational approaches for code-switched Speech and Natural Language Processing. We motivate why processing code-switched …

2019-03-25abs ↗pdf ↗

New algorithm learns switching dynamics from multiple neural signals.

problem Learning accurate switching dynamical system models from multimodal neural data.
method Unsupervised learning algorithm for multiscale switching dynamical system models.
result Switching multiscale dynamical system models outperform single-scale models in behavior decoding.

Many natural systems, such as neurons firing in the brain or basketball teams traversing a court, give rise to time series data with complex, nonlinear dynamics. We can gain insight into these systems by decomposing the data into segments that are each explained by simpler dynamic units. Building on switching linear dy…

2016-10-26abs ↗pdf ↗

Efficiently infers switching nonlinear systems with collapsed amortized variational inference.

problem Inference in switching nonlinear dynamical systems with discrete latent variables.
method Learn an inference network as a proposal for continuous latent variables, performing exact marginalization of discrete variables.
result Successfully segments time series data into meaningful regimes using piece-wise nonlinear dynamics.

Study optimal liquidation with multiple regimes using BSDEs with singular terminal values.

problem Optimal liquidation with regime switching in dark pools.
method Introduced a system of BSDEs with jumps and singular terminal values.
result Existence and uniqueness results for the BSDE system are obtained.

Many complex dynamical phenomena can be effectively modeled by a system that switches among a set of conditionally linear dynamical modes. We consider two such models: the switching linear dynamical system (SLDS) and the switching vector autoregressive (VAR) process. Our Bayesian nonparametric approach utilizes a hiera…

2010-03-19abs ↗pdf ↗

Breaks down complex nonlinear dynamics into simpler components.

problem Control of nonlinear dynamical systems remains challenging.
method Inspired by hybrid switching systems, decomposes dynamics into simpler stochastic switching linear dynamical systems.
result Extracts hierarchies of Markovian and auto-regressive locally linear controllers from nonlinear experts.

Proposes a new model for better speech segmentation.

problem Improving speech segmentation accuracy.
method Integrates recurrent explicit duration variables into rSLDS and uses Pólya-gamma augmentation for inference.
result Demonstrates improved segmentation on various datasets.

Study identifies and validates a method for system identification of Markov jump linear systems.

problem System identification for autonomous Markov jump linear systems with complete state observations.
method Proposes switched least squares method for identification and derives rates of convergence.
result Data-independent rate of convergence is O(log(T)/T)\mathcal{O}\big(\sqrt{\log(T)/T} \big), showing strong consistency.

New algorithm for continuous-time switching systems using variational inference.

problem Inference in time-series data with continuous-time switching systems.
method Developed a variational inference algorithm combining Gaussian process approximation and posterior inference for Markov jump processes.
result Bayesian latent state estimates and point estimates of unknown parameters for arbitrary points on the real axis.

Compact scheme solves American put options with regime-switching using finite differences and Hermite interpolation.

problem Pricing American put options with regime-switching model.
method Logarithmic transformation, compact finite difference scheme, Hermite interpolation.
result The scheme provides an accurate and fast solution compared to other methods.

Paper derives analytical formulas for NLD-CEV moments with regime switching.

problem Analytical tractability of NLD-CEV models under stochastic regimes.
method Hybrid system approach using Feynman-Kac formula for solving interconnected PDEs.
result Exact closed-form expressions for fractional-order conditional moments.

This work addresses identifiability in sequential data with switching dynamics, introducing a new estimator.

problem Identifiability of sequential data with regime-switching dynamics under flexible assumptions.
method Introduces ΩΩSDS, a flow-based estimator for exact likelihood optimization.
result Demonstrates improved disentanglement and more accurate forecasting compared to VAE-based estimators.

Investigates optimal portfolio selection with regime-switching-induced stock price shocks.

problem Mean-variance portfolio selection with regime-switching and stock price jumps.
method Modeling regime-switching and stock price jumps, deriving optimal portfolio strategy and efficient frontier using ODEs.
result Added complexity due to regime-switching-induced stock price shocks, leading to nonlinear ODEs.

The paper solves a complex control problem with stochastic elements and switching conditions.

problem Non-homogeneous stochastic LQ control with regime switching and random coefficients.
method Explicit optimal control and value obtained through two systems of backward stochastic differential equations (BSDEs). Existence and uniqueness of solutions proved using BMO martingales and contraction mapping method.
result Explicit optimal state feedback control and optimal value derived for the problem.

Framework models multiscale dynamics with Bayesian learning for regime changes.

problem Analyzing complex interactions between fast and slow processes.
method Hierarchical state-space modeling with Sequential Monte Carlo.
result Bayesian approach accurately tracks state transitions and identifies switching dynamics.

Optimal credit and consumption strategies in a switching market with default contagion.

problem Optimal portfolio and consumption decisions in a credit market with default contagion.
method Cobb-Douglas utility, recursive ODE system, backward solution from all-default state.
result Existence and uniqueness of optimal feedback controls, verification theorem.

The paper develops RL methods for optimal switching between multiple states.

problem Optimal switching between multiple states in continuous time.
method Entropy-regularized exploration, HJB equations, policy improvement, value function convergence.
result The RL algorithm converges to optimal policies as temperature parameter vanishes.

Estimates hybrid dynamical systems with polynomial expansions and Markovian switching.

problem Identifying hybrid dynamical systems with nonlinear autoregressive exogenous (NARX) components and Markovian switching.
method Probabilistic framework using Expectation Maximization for parameter estimation, including submodel coefficients, hidden state values, and transition probabilities. Disentangles mode classification and NARX regression tasks. Uses soft-labels and coordinate descent approach for parameter fitting.
result Demonstrated on a SMNARX problem with three nonlinear sub-models, achieving parsimonious models through l1-norm bridge estimation and hard-thresholding.

Paper tackles utility maximization with job-switching and retirement constraints.

problem Maximizing utility with job-switching and retirement constraints.
method Dual-martingale approach and double obstacle problem theory.
result Characterization of optimal job-switching strategy and wealth boundaries.

We introduce a machine learning approach for extracting fine-grained representations of protein evolution from molecular dynamics datasets. Metastable switching linear dynamical systems extend standard switching models with a physically-inspired stability constraint. This constraint enables the learning of nuanced repr…

2016-10-05abs ↗pdf ↗

Study optimal liquidation strategies with infinite horizon and regime switching.

problem Optimal liquidation with semimartingale strategies in a stochastic environment.
method Characterization of value function and optimal strategy via BSDEs with infinite horizon.
result Existence and uniqueness of optimal control problem solutions.

Unified framework detects change-points and estimates parameters in nonlinear systems with regime switching.

problem Detecting change-points and estimating parameters in nonlinear dynamical systems with regime transitions.
method Residual-loss anomaly analysis of physics-informed neural networks, two-stage strategy.
result The method outperforms traditional approaches in change-point localization and parameter estimation accuracy.

Researchers develop a method to control nonlinear systems with Koopman operator regression.

problem Controlling nonlinear systems with finite action spaces.
method Koopman operator regression for dynamics estimation and model predictive control for control.
result The method yields a linear switching predictive model for control.

Study optimal consumption and investment strategies with constraints in a market with random coefficients.

problem Optimal consumption and investment strategies with constraints in a regime switching market with random coefficients.
method Explicit optimal strategies provided via solutions to new BSDE systems.
result Solving new BSDEs to find optimal values and strategies.

This paper studies the optimal VIX futures trading problems under a regime-switching model. We consider the VIX as mean reversion dynamics with dependence on the regime that switches among a finite number of states. For the trading strategies, we analyze the timings and sequences of the investor's market participation,…

2016-05-25abs ↗pdf ↗

Latent force models (LFMs) are hybrid models combining mechanistic principles with non-parametric components. In this article, we shall show how LFMs can be equivalently formulated and solved using the state variable approach. We shall also show how the Gaussian process prior used in LFMs can be equivalently formulated…

2012-02-14abs ↗pdf ↗

SALT models combine ARHMM and SLDS for efficient, interpretable time-series analysis.

problem Efficient modeling of systems with time-varying dynamics and long-range dependencies.
method Switching autoregressive low-rank tensor models parameterized with a low-rank factorization.
result SALT models provide a balance of interpretability and efficiency, outperforming ARHMMs and SLDSs.

Paper presents an efficient algorithm for linear MDP with low switching cost.

problem Large state space reinforcement learning problems with low switching cost.
method First algorithm for linear MDP with low switching cost, achieving near-optimal regret and switching cost.
result Regret bound of $\widetilde{O}\left(\sqrt{d^3H^4K} ight)$ and near-optimal switching cost of $O\left(d H\log K ight)$.

In this paper, we consider a discrete time economy where we assume that the short term interest rate follows a quadratic term structure of a regime switching asset process. The possible non-linear structure and the fact that the interest rate can have different economic or financial trends justify the interest of Regim…

2013-05-13abs ↗pdf ↗

ITF improves DSR but inflates curvature, while marginal likelihood reduces it, affecting QoIs.

problem Curvature mismatch between teacher forcing and marginal likelihood in chaotic dynamical systems.
method Comparing objective-induced curvatures of ITF and marginal likelihood in a probabilistic switching augmentation of AL-RNNs.
result Curvature inflation by ITF and reduction by marginal likelihood affect dynamical quantities of interest.

Develops identifiability theory for multi-lag regime-switching models.

problem Ensuring interpretability of deep latent variable models with multi-lag dependencies.
method Formulates a general theoretical framework for multi-lag Regime-Switching Models (RSMs), proving identifiability of number of regimes and multi-lag transitions.
result Establishes identifiability conditions for multi-lag regime-switching models, including Markov Switching Models and Switching Dynamical Systems.

We study online learning when partial feedback information is provided following every action of the learning process, and the learner incurs switching costs for changing his actions. In this setting, the feedback information system can be represented by a graph, and previous works studied the expected regret of the le…

2018-10-23abs ↗pdf ↗

Develops a method to model neural dynamics with flexible yet interpretable latent states.

problem Capturing complex nonlinear dynamics in neural time series while maintaining interpretability.
method Gaussian Process Switching Linear Dynamical System (gpSLDS) that balances expressiveness and interpretability.
result Favorable performance in comparison to rSLDS on synthetic and real neuroscience data.