The paper tackles three financial issues: time resolution, nonstationarity, and latent factors.
problem Three fundamental issues in financial data: time resolution, nonstationarity, and latent factors.
method A causal perspective to reexamine and solve these issues.
result Provides systematic solutions to financial data issues.
Two new PCA variants improve financial data analysis.
problem Numerical instability and nonstationarity in PCA for finance.
method Iterated and exponentially weighted moving PCA variants using Ogita-Aishima iteration.
result Improved stability and adaptability in financial data analysis.
Develops methods to test nonstationarity and detect change points in RL.
problem Nonstationary environments in reinforcement learning.
method Consistent testing procedure and change point detection method.
result Validates the effectiveness of the proposed methods in real data.
MPS selects models for nonstationary time series in real-time.
problem Model selection under nonstationary time series.
method Combines conformal inference with model confidence sets.
result Reliably identifies optimal models under nonstationarity.
Develops deep models to handle nonstationary spatial extremal dependence.
problem Challenges in modeling nonstationary extremal dependence in spatial data.
method Deep compositional spatial models to capture nonstationarity in extremal dependence.
result Efficient estimation of warped space for nonstationary spatial data.
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.
The paper provides bounds on the CDF of a variable under nonstationary conditions.
problem Estimating the complete distribution of a random variable under nonstationary conditions.
method Time-uniform and value-uniform bounds on the CDF of the running averaged conditional distribution.
result Presented computationally efficient bounds that are always valid and sometimes trivial.
Although nonstationary data are more common in the real world, most existing causal discovery methods do not take nonstationarity into consideration. In this letter, we propose a kernel embedding-based approach, ENCI, for nonstationary causal model inference where data are collected from multiple domains with varying d…
Study shows nonstationary bandits require T-dependent regret even with minimal nonstationarity.
problem Understanding satisficing regret in nonstationary multi-armed bandits.
method Developed a novel Fano-based framework for nonstationary bandits with a post-interaction reference construction.
result Optimal regret scales with T even with minimal nonstationarity, contrasting with stationary case.
Online learning rbfnet improves multi-horizon returns forecasts for financial time series.
problem Nonstationarity and concept drift in financial time series.
method Combines feature representation transfer with sequential optimisation.
result Online learning rbfnet outperforms random-walk and batch learners.
An ensemble of randomized NNs improves time series forecasting accuracy.
problem Forecasting time series with multiple seasonality and nonstationarity.
method Randomized neural networks with pattern-based time series representation and diversity control strategies.
result Outperforms statistical and machine learning models in forecasting accuracy.
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…
New method disentangles latent variables in nonstationary data.
problem Disentangling latent variables in nonstationary sequential data.
method NCTRL framework exploiting Markov assumption and temporal structure.
result Independent latent components can be recovered from nonlinear mixture without auxiliary variables.
Policy gradient and actor-critic algorithms form the basis of many commonly used training techniques in deep reinforcement learning. Using these algorithms in multiagent environments poses problems such as nonstationarity and instability. In this paper, we first demonstrate that standard softmax-based policy gradient c…
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…
Method regularizes Cholesky factors to detect nonstationarity in longitudinal data.
problem Detecting nonstationarity in large covariance matrices of longitudinal data.
method Fused-Lasso regularization on Cholesky factors.
result Regularization leads to smooth subdiagonals, indicating nonstationarity.
Researchers have constantly asked whether stock returns can be predicted by some macroeconomic data. However, it is known that macroeconomic data may exhibit nonstationarity and/or heavy tails, which complicates existing testing procedures for predictability. In this paper we propose novel empirical likelihood methods …
RoME optimizes mobile health interventions by modeling user and time-specific effects.
problem Challenges in optimizing mobile health interventions due to participant heterogeneity, nonstationarity, and nonlinear relationships.
method RoME uses a Robust Mixed-Effects contextual bandit algorithm with random effects, network cohesion penalties, and debiased machine learning.
result RoME achieves robust regret bounds even with complex baseline rewards, demonstrating superior performance in simulations and studies.
New method improves stock return prediction in non-stationary markets.
problem Tackles the challenge of predicting stock returns in non-stationary environments.
method Jointly optimizes model class and training window size using a tournament procedure.
result Consistently outperforms standard benchmarks by 14-23% in out-of-sample R2. BRPC online Bayesian calibration handles gradual and abrupt system changes.
problem Aligning model outputs with field observations in evolving systems.
method Bayesian Recursive Projected Calibration (BRPC) for streaming data under simulator mismatch and nonstationarity.
result Improves calibration accuracy under gradual changes and robustness under abrupt regime shifts.
This paper presents a novel approach for approximate integration over the uncertainty of noise and signal variances in Gaussian process (GP) regression. Our efficient and straightforward approach can also be applied to integration over input dependent noise variance (heteroscedasticity) and input dependent signal varia…
The paper tackles stock prediction models by improving their generalizability to out-of-sample domains using causal representation learning.
problem Low signal-to-noise ratio and nonstationary nature of financial markets lead to poor performance of stock prediction models.
method The paper investigates Domain Generalization techniques, focusing on causal representation learning to improve model generalizability. It introduces a novel error bound and a causal discovery technique to mitigate spurious correlations.
result The proposed approach enhances the generalizability of stock prediction models, as demonstrated by numerical results.
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.
Power supply from renewable resources is on a global rise where it is forecasted that renewable generation will surpass other types of generation in a foreseeable future. Increased generation from renewable resources, mainly solar and wind, exposes the power grid to more vulnerabilities, conceivably due to their variab…
ProFnet models HDFTS with neural networks, offering scalable probabilistic forecasts.
problem Modeling high-dimensional functional time series with nonlinear trends and high spatial dimensions.
method Integrates feedforward and deep neural networks with probabilistic modeling.
result Superior performance in forecasting Japan's mortality rates.
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.
Nonlinear independent component analysis (ICA) provides an appealing framework for unsupervised feature learning, but the models proposed so far are not identifiable. Here, we first propose a new intuitive principle of unsupervised deep learning from time series which uses the nonstationary structure of the data. Our l…
Recent work in distance metric learning has focused on learning transformations of data that best align with provided sets of pairwise similarity and dissimilarity constraints. The learned transformations lead to improved retrieval, classification, and clustering algorithms due to the better adapted distance or similar…
Study incentivizes exploration in non-stationary MAB with compensation.
problem Incentivized exploration for non-stationary stochastic bandits with biased feedback.
method Proposed algorithms for abruptly-changing and continuously-changing non-stationary environments.
result Achieves sublinear regret and compensation over time.
FFRK automatically extracts features for spatial interpolation without external variables.
problem Spatial interpolation challenges, especially nonstationarity and lack of explanatory variables.
method Feature-Free Regression Kriging (FFRK) method that extracts geospatial features.
result FFRK outperforms classical methods in predicting heavy metal concentrations.
Bayesian optimization has shown to be a fundamental global optimization algorithm in many applications: ranging from automatic machine learning, robotics, reinforcement learning, experimental design, simulations, etc. The most popular and effective Bayesian optimization relies on a surrogate model in the form of a Gaus…
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.
Contextual multi-armed bandit (MAB) algorithms have been shown promising for maximizing cumulative rewards in sequential decision tasks such as news article recommendation systems, web page ad placement algorithms, and mobile health. However, most of the proposed contextual MAB algorithms assume linear relationships be…
The paper analyzes prediction error in nonstationary settings using weighted risk minimization.
problem Prediction under distribution drift and nonstationary conditions.
method General decomposition of excess risk into learning and drift terms, proving oracle inequalities under mixing conditions.
result Oracle inequalities for the learning error, providing bounds that hold uniformly over arbitrary weight classes.
In the analysis of sequential data, the detection of abrupt changes is important in predicting future changes. In this paper, we propose statistical hypothesis tests for detecting covariance structure changes in locally smooth time series modeled by Gaussian Processes (GPs). We provide theoretically justified threshold…
We report an empirical study of the Ibovespa index of the Sao Paulo Stock Exchange in which we detect the existence of long-range correlations. To analyze our data we introduce a rescaled variant of the usual Detrended Fluctuation Analysis that allows us to obtain the Hurst exponent through a one-parameter fitting. We …
Paper proposes a method for efficient online classification using siamese networks and active learning.
problem Online learning challenges with limited, nonstationary, and imbalanced data.
method Synergistic combination of siamese neural networks and active learning with multi-sliding window approach.
result Significantly outperforms baselines and state-of-the-art algorithms in terms of learning speed and performance.
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…
New framework for disentangling features from noisy data.
problem Disentangling identifiable features from noisy data.
method Structured Nonlinear Independent Component Analysis (SNICA).
result Identifiability holds even in the presence of noise of unknown distribution.
LEAP identifies latent causal variables from temporal data.
problem Recovering time-delayed latent causal variables from general temporal data.
method Proposes LEAP, a framework that extends VAEs with constraints for temporally causal latent processes.
result Successfully identifies temporally causal latent processes from observed variables under various dependency structures.
Proposes a method to adapt models in nonstationary environments using ℓ1 regularization.
problem Adapting models to nonstationary environments in machine learning.
method Integrates ℓ1 regularization of differences between source and target parameters.
result Effective balance of stability and plasticity in model adaptation.
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.
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.
Estimates financial networks using high-frequency trade data.
problem Leverage high-resolution intraday trade data for financial network insights.
method Estimate financial networks using random forests with microstructure measures.
result Higher network density in 2007, with Lehman Brothers having high degree connectivity.
This paper tackles online reinforcement learning for unseen tasks with unknown boundaries.
problem Real-world tasks violate assumptions of task distributions, independence, and clear task delineations.
method A mixture of Gaussian Processes models different dynamics, and a transition prior handles temporal dependencies.
result The approach reliably handles task distribution shifts and outperforms alternatives in non-stationary tasks.
Recent work in distance metric learning has focused on learning transformations of data that best align with specified pairwise similarity and dissimilarity constraints, often supplied by a human observer. The learned transformations lead to improved retrieval, classification, and clustering algorithms due to the bette…
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.
New algorithms improve DRSL for large-scale problems.
problem Distributionally robust learning for real-world applications.
method Variance-reduced stochastic extra-gradient algorithms for min-max optimization.
result Provable faster convergence rates than existing approaches.