New definition of joint stationarity improves process recovery over graphs.
problem Regression tasks with high-dimensional multivariate processes dependent on graph topology.
method Introduces joint stationarity, a new definition that reduces estimation variance and complexity.
result One reliably learns covariance structure from a single realization and solves MMSE problems nearly linearly in time.
This paper introduces joint stationarity for time-varying graph signals, improving signal processing accuracy.
problem Lack of consideration for both time and vertex dimensions in graph-based signal processing.
method Introduces joint (time-vertex) stationarity, a scalable Wiener optimization framework for joint denoising and learning.
result Joint stationarity optimally improves signal processing accuracy, as shown by real data experiments.
New method predicts evolving graph signals with less complexity.
problem Predicting time-evolving graph signals using existing methods is challenging.
method Introduces a joint stationarity framework for time-vertex processes to predict graph signals.
result Outperforms existing methods and achieves similar accuracy with lower complexity.
Modeling joint log-volatility dynamics with multivariate fractional Ornstein-Uhlenbeck process.
problem Empirical evidence of joint behavior in realized volatility time series.
method Multivariate fractional Ornstein-Uhlenbeck process with different Hurst exponents and non-trivial interdependencies.
result Model accurately captures asymmetries and spillover effects in realized-volatility time series.
Study analyzes stock market correlations using multivariate distributions.
problem Capturing the correlation structure of complex, non-stationary systems.
method Applied Random Matrix Model to empirical data of 479 US stocks.
result Described and quantified changes in empirical distributions due to non-stationarity.
New rule universally consistent for online learning with non-ergodic data.
problem Online learning with non-ergodic data processes.
method Developed an online learning rule for processes on (X,Y) pairs.
result Generalizes past results to non-ergodic processes on (X,Y).
The paper analyzes heavy-tailed multivariate distributions in non-stationary systems using random matrix theory.
problem Risk assessment for rare events in complex, non-stationary systems.
method Generalized scalar product between correlation matrices, model for non-stationary fluctuations.
result Formulae for multivariate distributions with reduced parameters, facilitating applications.
TSFMs embed non-stationary time series data, revealing specific types of changes.
problem Understanding non-stationarity in TSFMs' embedding spaces.
method Examined mean shifts, variance changes, linear trends, and persistence in TSFMs.
result Different TSFMs exhibit distinct failure modes in detecting non-stationarity.
Study forecasts U.S. bond index using deep learning, finding persistence is key.
problem Forecasting U.S. aggregate bond index with deep learning methods.
method Constructed a stationary but maximally persistent representation of the bond index, evaluated using MLPs and CNNs.
result Deep learning models outperform traditional methods in short-horizon forecasting of bond indices.
Deep RL agents suffer from transient non-stationarity, which ITER mitigates.
problem Transient non-stationarity in deep RL agents affects generalization.
method Iterated Relearning (ITER) transfers knowledge between networks to reduce non-stationarity.
result ITER improves deep RL agents' performance on generalization benchmarks.
TimeBridge addresses non-stationarity in long-term time series forecasting.
problem Non-stationarity in multivariate time series leads to spurious regressions and obscures long-term relationships.
method TimeBridge segments series into patches, applying Integrated Attention for short-term non-stationarity and Cointegrated Attention for long-term cointegration.
result TimeBridge achieves state-of-the-art performance in both short-term and long-term forecasting.
Maxwell fields inherit stationarity from stationary metrics.
problem Maxwell fields in asymptotically flat solutions of Einstein-Maxwell equations.
method Proving Maxwell fields inherit stationarity of the metric.
result Maxwell fields inherit stationarity from stationary metrics.
This paper compares stationarity in Bitcoin and S&P500 price indices.
problem Comparing stationarity in cryptocurrency and traditional stock market indices.
method Wide sense stationarity defined; Wiener-Khinchin Theorem applied; stationarity achieved through detrending and normalization of price returns.
result S&P500 price return achieves stationarity for 28 years with specific normalization windows, while Bitcoin's stationarity varies by segment and volatility.
Algorithm helps first agent learn to collaborate with adaptive second agent in MDPs.
problem Designing a learning algorithm for the first agent to collaborate with an adaptive second agent in MDPs.
method Novel online learning algorithms for the first agent with a specific regret bound.
result Sub-linear regret of the first agent implies near-optimality of the joint return for smooth MDPs.
CW-Gen models improve probabilistic time series forecasting by incorporating prior information.
problem Challenges in probabilistic forecasting of multivariate time series due to non-stationarity, inter-variable dependencies, and distribution shifts.
method CW-Gen framework that incorporates prior information through conditional whitening. JMCE learns conditional mean and covariance, improving sample quality.
result CW-Gen consistently enhances predictive performance, capturing non-stationary dynamics and inter-variable correlations more effectively than prior-free approaches.
This paper improves multi-agent reinforcement learning by distinguishing non-stationary samples based on likelihood.
problem Non-stationarity in decentralized multi-agent reinforcement learning environments.
method Likelihood Quantile Networks (LQN) that distinguish non-stationary samples based on likelihood of returns.
result LQN leads to more stable, sample-efficient, and convergent joint optimal policies compared to previous methods.
Develops a test for strict stationarity in stochastic processes.
problem Testing strict stationarity of discrete time stochastic processes.
method Window averaged sample estimate of second order cumulant spectrum, asymptotic complex standard normal distribution test.
result Test statistic derived and demonstrated with 137Cs gamma ray decay data.
The paper explores various stationarity concepts in non-smooth optimization.
problem Understanding stationarity in non-smooth optimization problems.
method Introduction and discussion of different stationarity concepts for non-convex non-smooth functions.
result Clarification of the relationship among different stationarity concepts and their relevance in iterative methods.
Improved stock return prediction model handles noise and non-stationarity.
problem Predicting stock returns with robustness to noise and non-stationarity.
method Extended AROW algorithm to handle synchronous mini-batch updates and applied it to stock return prediction.
result The new model outperforms classical approaches in backtesting on S\&P500 stocks.
DoGS improves Gibbs sampling quality with variable selection orders and bounds.
problem Improving Gibbs sampler scan quality.
method Using Dobrushin influence to optimize Gibbs sampling.
result DoGS delivers higher-quality inferences with smaller sampling budgets.
The recent liberalization of the electricity and gas markets has resulted in the growth of energy exchanges and modelling problems. In this paper, we modelize jointly gas and electricity spot prices using a mean-reverting model which fits the correlations structures for the two commodities. The dynamics are based on Or…
The paper extends stationarity to graph signals, providing new estimation methods.
problem Developing flexible models for signals on graphs.
method Generalizing stationarity to graph signals, introducing graph localization operator and Power Spectral Density.
result Stationary graph signals have a well-defined Power Spectral Density that can be efficiently estimated.
Bayesian method models financial time series with non-stationarity and dependency.
problem Discrimination between non-stationarity and long-range dependency in financial time series.
method Adaptive spectral technique using non-parametric Bayesian inference with Reversible Jump Markov Chain Monte Carlo.
result Bayesian method effectively models both long-range dependency and non-stationarity in financial time series.
This paper surveys methods to handle non-stationarity in multi-agent deep reinforcement learning.
problem Non-stationarity in multi-agent reinforcement learning environments.
method Modifications in training procedures, opponent policy representation learning, meta-learning, communication, and decentralized learning.
result A comprehensive review of recent works on addressing non-stationarity in multi-agent deep reinforcement learning.
Master algorithm fails to detect non-stationarity in practical settings.
problem Non-Stationary Reinforcement Learning without prior knowledge.
method Master algorithm tested under various conditions, including piecewise stationary multi-armed bandits.
result Master's non-stationarity detection is ineffective for practical horizons, leading to performance similar to random restarting.
Study classifies stock price data into stationary and non-stationary periods for mechanical trading.
problem Classifying stock price fluctuations into stationary and non-stationary periods for trading.
method Stationarity analysis using KM2O-Langevin theory and trend-based indicators for stationary periods, oscillator-based indicators for non-stationary periods. result Back testing confirms the strategy is a safe trading strategy with small maximum drawdown.
Study large deviation in stationarized fully lifted blirp interpolation.
problem Understanding atypical solutions in random optimization problems.
method Large deviation theory applied to fully lifted blirp interpolation.
result Elegant relations uncovered for fundamental interpolating parameters.
Gaussian process is a theoretically appealing model for nonparametric analysis, but its computational cumbersomeness hinders its use in large scale and the existing reduced-rank solutions are usually heuristic. In this work, we propose a novel construction of Gaussian process as a projection from fixed discrete frequen…
Study non-stationary bandits with resource constraints.
problem Maximize reward in a non-stationary environment with resource constraints.
method Propose new non-stationarity measure and use primal-dual analysis.
result Upper and lower bounds for non-stationary BwK problem.
Neural population activity often exhibits rich variability and temporal structure. This variability is thought to arise from single-neuron stochasticity, neural dynamics on short time-scales, as well as from modulations of neural firing properties on long time-scales, often referred to as "non-stationarity". To better …
Framework isolates causal effects from time series data, improving accuracy under non-stationarity and autocorrelation.
problem Causal inference in non-stationary, autocorrelated time series data.
method Decomposes time series into trend, seasonal, and residual components; performs component-specific causal analysis.
result Framework more accurately recovers ground-truth causal structure than state-of-the-art baselines, especially under strong non-stationarity and temporal autocorrelation.
Study uses RL to optimize dynamic portfolios, addressing non-stationarity and constraints.
problem Non-stationarity and investment constraints in dynamic portfolio optimization.
method Reinforcement learning with regime change variables and practical constraints integration.
result Enhanced prediction accuracy through incorporation of regime change variables.
Paper tackles uncertainty prediction for deep sequential regression.
problem Challenges in generating accurate uncertainty estimates for deep recurrent networks.
method Flexible method that generates symmetric and asymmetric uncertainty estimates without stationarity assumptions.
result Outperforms competitive baselines on both drift and non-drift scenarios.
SplitSGD dynamically adjusts learning rate based on stationarity detection.
problem Optimizing learning rates for stochastic optimization.
method SplitSGD uses a simple stationarity detection method to adjust learning rates.
result SplitSGD improves generalization and outperforms other adaptive methods.
Develops RL algorithm for lifelong non-stationary environments.
problem Challenges of reinforcement learning in environments with persistent change.
method Formalizes lifelong non-stationarity, uses latent variable models, and leverages online learning and probabilistic inference.
result Substantial improvement in performance over non-reasoning approaches in lifelong non-stationary environments.
New method for estimating and optimizing MDPs without stationarity.
problem Challenges in offline contextual MDP estimation without stationarity.
method Introduces a new adaptive estimation and cost optimization approach for contextual MDPs.
result First robust, theoretically backed method for offline contextual MDP estimation.
New conditions for ACD model consistency and normality.
problem Random number of durations in ACD model.
method Additional sufficient conditions for consistency and normality of QMLE.
result Finite mean of durations is required for consistency and normality.
This paper explores twisted Lagrangian tori in C^2 and their Hamiltonian stationarity.
problem Understanding the Hamiltonian stationarity of twisted Lagrangian tori in C^2.
method Investigation of differential geometry of twisted tori, including product and Chekanov's exotic tori.
result Only product tori are minimal under Hamiltonian deformations, indicating Chekanov's exotic tori are not area minimal.
Study on fake stationary Volterra Heston model for non-stationary processes.
problem Non-stationary nature of true Volterra equations.
method Weak notion of stationarity (fake stationary regime) for inhomogeneous affine Stochastic Volterra equations.
result Existence of limiting distributions in the long run, which may depend on initial state.
RD-Agent(Q) automates quantitative finance research and development.
problem Challenges in asset return prediction due to high dimensionality and volatility.
method Data-centric multi-agent framework for automated research and development of quantitative strategies.
result Up to 2X higher annualized returns with 70% fewer factors.
Proposes a probabilistic framework for stationary topological signals on simplicial complexes.
problem Complex data structures require new models and tools.
method Generalizes stationarity to topological signals on simplicial complexes.
result Defines topological power spectral density (PSD) for stationary signals.
Meta-learning technique speeds RL control learning and handles non-stationarity.
problem Hyperparameter tuning and non-stationarity in RL control.
method Meta-gradient descent for online step-size tuning with eligibility traces.
result Meta-step-size parameter easy to set, speeds learning, and handles non-stationarity.
Solves POMDPs with recurrent neural networks and natural policy gradient.
problem Non-stationarity in optimal policies of POMDPs.
method Integrates recurrent neural networks into natural policy gradient and temporal difference learning.
result Non-asymptotic theoretical guarantees for global optimality up to function approximation.
Flexible non-stationary modeling of spatial outcomes using a mixed-stationary Gaussian process.
problem Limited flexibility in non-stationary models and computational intractability.
method Developed a non-stationary Gaussian process with individually set stationarity parameters at each location, using a non-parametric mixture model to reduce parameters and incorporate spatial correlation.
result Improved prediction efficiency through spatially correlated components in the mixture model.
A new QHR model extends HR model with a quadratic variance function.
problem Modeling volatility with greater flexibility and stationarity.
method Introducing a quadratic variance function to the HR model, maintaining Markovian property.
result Stationary distribution of the QHR model is Pearson type IV.
S&P 500 index data sampled at one-minute intervals over the course of 11.5 years (January 1989- May 2000) is analyzed, and in particular the Hurst parameter over segments of stationarity (the time period over which the Hurst parameter is almost constant) is estimated. An asymptotically unbiased and efficient estimator …
A simple baseline outperforms deep learning methods in transportation forecasting.
problem The importance of stationarity and recurrent patterns in transportation data.
method A naive baseline based on average weekly patterns and linear regression.
result The baseline method achieves comparable or better results than state-of-the-art deep learning approaches.
Improved analysis for fair federated learning reduces dependence on noise floor.
problem Asymptotic stationarity in group fair federated learning with reduced noise floor dependence.
method DS FedProxGrad framework with inexact local proximal solutions and fairness regularization.
result Algorithm converges asymptotically to stationarity without dependence on a noise floor.