New method for identifying causal relationships in financial time series data.
problem Identifying causal relationships in nonstationary financial time series data.
method Refined constraint-based causal discovery algorithm (CD-NOTS) for nonstationary time series data.
result CD-NOTS effectively identifies causal connections in financial applications.
This paper improves risk control for financial markets by calibrating VaR forecasts using conformal methods.
problem Nonstationary and regime-dependent losses in financial markets.
method Regime-weighted conformal risk control (RWC) for VaR forecasting.
result RWC improves regime-conditional stability in some settings with modest conservativeness changes.
Enhanced LSTM predicts equity trends, outperforming traditional methods.
problem Nonstationary and nonlinear market regimes challenge trend forecasting.
method LSTM-based framework for forecasting equity trend differences.
result LSTM framework outperforms traditional methods in terms of overall PNL.
The condition for stationary increments, not scaling, detemines long time pair autocorrelations. An incorrect assumption of stationary increments generates spurious stylized facts, fat tails and a Hurst exponent H_s=1/2, when the increments are nonstationary, as they are in FX markets. The nonstationarity arises from s…
Algorithm ranks assets in fluctuating markets.
problem Ranking assets in nonstationary time series.
method Naive Bayes asset ranker that adjusts weights based on performance.
result Outperforms traditional methods and S&P 500 index.
Paper proposes a new portfolio model for better investment decisions.
problem Traditional portfolio models fail to adapt to nonstationary markets.
method Developed a mean-detrended cross-correlation portfolio model (M-DCCP model).
result The M-DCCP model outperforms traditional models in constructing optimal portfolios.
Adaptive estimation of alpha-Stable distribution and Hurst exponent for nonstationary time series.
problem Nonstationary time series require adaptive models to avoid bias.
method Moving estimator with exponentially weakening weights of old values, optimized using EMA of absolute central moments.
result Continuous adaptive estimation of alpha-Stable distribution and Hurst exponent for market stability evaluation.
Adaptive t-distribution estimates nonstationary time series using moving moments.
problem Nonstationary time series with varying dependence structure.
method Moving estimator optimizing a weighted log-likelihood, using exponential moving averages for moments.
result Evolution of ν parameter in Student's t-distribution, capturing tail behavior and extreme events.
We analyze the question whether sliding window time averages applied to stationary increment processes converge to a limit in probability. The question centers on averages, correlations, and densities constructed via time averages of the increment x(t,T)=x(t+T)-x(t)and the assumption is that the increment is distribute…
Study uses detrended cross-correlation to analyze cryptocurrency market, revealing robust collective modes and distinguishing interdependencies.
problem Nonstationarity, long-range memory, and heavy-tailed fluctuations obscure traditional correlations in complex systems.
method Constructs detrended correlation matrices using multifractal detrended cross-correlation coefficient ρr to emphasize different fluctuations. result Detrending and fluctuation analysis reveal distinct spectral properties from random case, identifying market and sectoral components.
Flexible nonstationary Gaussian process with neural network parameters.
problem Limited expressiveness of stationary Gaussian processes.
method Nonstationary kernels with neural network parameters trained jointly.
result Better accuracy and log-score compared to stationary and hierarchical models.
The method of cointegration in regression analysis is based on an assumption of stationary increments. Stationary increments with fixed time lag are called integration I(d). A class of regression models where cointegration works was identified by Granger and yields the ergodic behavior required for equilibrium expectat…
Unified formulation bridges adversarial and nonstationary bandits.
problem Handling time-varying reward distributions in multi-armed bandit problems.
method Unified oracle that switches between adversarial and nonstationary bandit oracles based on window size.
result Optimal regret achieved with matching lower bound.
Study introduces AMVP and AMRR for dynamic portfolio optimization in volatile markets.
problem Optimizing portfolios in volatile and nonstationary financial markets.
method Adaptive Minimum-Variance Portfolio (AMVP) framework with ARFIMA-FIGARCH processes and non-Gaussian innovations.
result Demonstrated superior performance in risk reduction and portfolio stability during market breaks.
A new approach learns to represent context for nonstationary bandits.
problem Nonstationary contextual bandits where patterns change over time.
method Combines recurrent neural networks with contextual linear bandit algorithm.
result Consistently outperforms handcrafted historical contexts and other methods.
The analysis of nonstationary time series is of great importance in many scientific fields such as physics and neuroscience. In recent years, Gaussian process regression has attracted substantial attention as a robust and powerful method for analyzing time series. In this paper, we introduce a new framework for analyzi…
Model nonstationary spatial processes using normalizing flows.
problem Difficult selection of spatial warping functions.
method Neural autoregressive flows (NAFs) for complex, high-dimensional warpings.
result NAFs model has greater representational capacity than other spatial process models.
New algorithm for nonstationary multi-armed bandits with optimal performance.
problem Nonstationary multi-armed bandits with changing model parameters over time.
method Adaptive Resetting Bandit (ADR-bandit) algorithm using adaptive windowing techniques.
result ADR-bandit achieves nearly optimal performance in both abrupt and gradual changes.
ConvNets improve nonstationary covariance estimation for large-scale spatial data.
problem Estimating nonstationary spatial covariance functions on large scales.
method Convolutional Neural Networks (ConvNets) for subregion identification and selection.
result Enhanced accuracy in parameter estimation using ConvNet-based partitioning.
SORSCNs improve nonstationary data modeling by self-organizing and adjusting network parameters.
problem Nonstationary data challenges traditional models in continuous learning.
method SORSCNs autonomously adjust network parameters and structure in real-time using adaptive algorithms.
result SORSCNs outperform other models in generalizing to nonstationary data.
We propose a method to clean covariance matrices of nonstationary systems by using time-independent eigenvalues.
problem Noise in covariance matrices of nonstationary systems with time-independent eigenvalues.
method Data-driven approach to use independent eigenvalues encoding long-term influence of future on present.
result Our method outperforms optimal stationary methods for filtering covariance matrix and its inverse.
New method identifies nonstationary causal structures in time series data.
problem Identifying causal relationships in time series data that change over time.
method High-order Markov Switching Models for regime-dependent causal discovery.
result Scalable approach for estimating high-order regime-dependent causal structures.
It is commonplace to encounter heterogeneous or nonstationary data, of which the underlying generating process changes across domains or over time. Such a distribution shift feature presents both challenges and opportunities for causal discovery. In this paper, we develop a framework for causal discovery from such data…
The model describing market dynamics after a large financial crash is considered in terms of the stochastic differential equation of Ito. Physically, the model presents an overdamped Brownian particle moving in the nonstationary one-dimensional potential U under the influence of the variable noise intensity, dependin…
SyMPLER improves time series forecasting in nonstationary environments with explainable models.
problem Nonstationary time series forecasting with limited interpretability.
method Dynamic piecewise-linear approximations based on Statistical Learning Theory generalization bounds.
result SyMPLER achieves comparable performance to black-box and explainable models while maintaining interpretability.
Saddle-point optimization problems are an important class of optimization problems with applications to game theory, multi-agent reinforcement learning and machine learning. A majority of the rich literature available for saddle-point optimization has focused on the offline setting. In this paper, we study nonstationar…
Markovian RNN adapts to nonstationary data using HMM for better time series prediction.
problem Nonstationary sequential data in real-life applications.
method Markovian RNN with HMM for regime switching and end-to-end optimization.
result Significant performance gains over vanilla RNN and Markov Switching ARIMA.
AREBA algorithm improves learning from imbalanced, nonstationary data.
problem Learning from imbalanced, nonstationary data in online settings.
method Adaptive REBAlancing (AREBA) algorithm that selectively includes examples to maintain class balance.
result AREBA significantly outperforms other algorithms in learning speed and quality.
BerlinUCB learns from episodic rewards in nonstationary contexts.
problem Online learning with episodic rewards in nonstationary environments.
method BerlinUCB integrates clustering for self-supervision.
result BerlinUCB outperforms standard contextual bandits in various scenarios.
A novel nonstationary permanental process relaxes kernel constraints and captures complex data patterns.
problem Limitations of existing permanental processes in terms of kernel types and stationarity.
method Sparse spectral representation of nonstationary kernels and hierarchical stacking of spectral feature mappings.
result Enhanced model expressiveness and reduced computational complexity.
Study analyzes stock market dynamics using recurrence measures and transitions.
problem Understanding transitions in stock market dynamics during crises.
method Recurrence plots and networks from nonstationary stock market data.
result Recurrence measures capture transitions in stock market dynamics.
Most real world phenomena such as sunlight distribution under a forest canopy, minerals concentration, stock valuation, exhibit nonstationary dynamics i.e. phenomenon variation changes depending on the locality. Nonstationary dynamics pose both theoretical and practical challenges to statistical machine learning algori…
Financial markets are systems with the complex behavior, that can be hardly analyzed by means of linear methods. Recurrence Quantification Analysis (RQA) is a nonlinear methodology, which is able to work with the nonstationary and short data series. Thus, we apply RQA for the studying of the critical events on financia…
New method infers causal relationships from nonstationary time series data.
problem Challenges in inferring causal relationships from nonstationary time series data.
method Proposes a new class of restricted SCM with time-varying filters and stationary noise, leveraging asymmetry from nonstationarity.
result Demonstrates effectiveness of the proposed methodology on various synthetic and real datasets.
Optimizes spectral density estimation for stationary and nonstationary processes.
problem Estimating spectral density of time series with complex structure.
method Optimally adaptive Bayesian spectral density estimation using smoothing spline covariance structure.
result Optimal eigendecomposition provides superior performance compared to alternative covariance functions.
Develops nonstationary MOGP kernels for better performance.
problem Limited applicability of existing MOGP kernels for nonstationary data.
method Harmonizable spectral mixture kernels for nonstationary MOGP.
result Automatic identification of nonstationary behavior in data.
New RL algorithm tackles nonstationary MDPs with linear approximations and varying rewards.
problem Nonstationary reinforcement learning with evolving reward and state transition functions.
method Developed a new algorithm LSVI-UCB-Restart with periodic restart, and parameter-free Ada-LSVI-UCB-Restart for unknown variation budgets.
result First minimax dynamic regret lower bound for nonstationary linear MDPs and linear MDPs lower bound.
New method tests independence with single nonstationary time series.
problem Testing independence in nonstationary nonlinear time series.
method Time-varying nonlinear regression, local long-run covariance estimation, strong Gaussian approximation.
result First framework for conditional independence testing with a single realization of a nonstationary nonlinear process.
This review analyzes RL in finance, highlighting its advantages and challenges.
problem Complex financial decision-making problems where traditional methods fail.
method Systematic review of 167 articles from 2017-2025, focusing on market making, portfolio optimization, and algorithmic trading.
result RL offers advantages over traditional methods, particularly in market making, but challenges remain.
New method optimizes portfolios for non-stationary markets.
problem Inadequate classical portfolio optimization for non-stationary markets.
method Reformulate portfolio optimization in spectral domain, using complex statistics.
result Time-varying optimal capital allocations for non-stationary markets.
New algorithm for nonstationary GLBs reduces computation and memory costs.
problem Nonstationary generalized linear bandits with unknown time-varying parameters.
method Discounted Online Mirror Descent (DOMD) for parameter estimation.
result Dynamic regret bounds of order O(1) per round in drifting and piecewise-stationary environments. A new method warps inputs to learn nonstationary kernels efficiently.
problem Learning nonstationary patterns in data with varying smoothness.
method Sparse spectrum Gaussian processes with input warping as conditional Gaussian measures.
result Efficient learning of nonstationary patterns with fewer parameters.
Most environmental phenomena, such as wind profiles, ozone concentration and sunlight distribution under a forest canopy, exhibit nonstationary dynamics i.e. phenomenon variation change depending on the location and time of occurrence. Non-stationary dynamics pose both theoretical and practical challenges to statistica…
In many scientific fields, such as economics and neuroscience, we are often faced with nonstationary time series, and concerned with both finding causal relations and forecasting the values of variables of interest, both of which are particularly challenging in such nonstationary environments. In this paper, we study c…
Bandit algorithms have been predominantly analyzed in the convex setting with function-value based stationary regret as the performance measure. In this paper, motivated by online reinforcement learning problems, we propose and analyze bandit algorithms for both general and structured nonconvex problems with nonstation…
The paper provides bounds for regression schemes using nonstationary training samples.
problem Developing confidence intervals for nonparametric regression with nonstationary data.
method The approach involves Rademacher and Vapnik-Chervonenkis theories to analyze the cost and optimality of regression schemes.
result The paper establishes nonasymptotic bounds for regression schemes and optimality in L2-distance. Develops a new method to discover causal relationships from nonstationary time series data.
problem Challenges in inferring causal relationships from observational data, especially for nonstationary time series.
method State-Dependent Causal Inference (SDCI) for conditionally stationary time series.
result SDCI can recover underlying causal dependencies with provable identifiability for state-dependent causal structures.
We present online prediction methods for time series that let us explicitly handle nonstationary artifacts (e.g. trend and seasonality) present in most real time series. Specifically, we show that applying appropriate transformations to such time series before prediction can lead to improved theoretical and empirical p…