arXiv research

A method based on wavelet transform and genetic programming is proposed for characterizing and modeling variations at multiple scales in non-stationary time series. The cyclic variations, extracted by wavelets and smoothened by cubic splines, are well captured by genetic programming in the form of dynamical equations. …

Transformers achieve near-optimal dynamic regret in non-stationary reinforcement learning.

We utilize a recently developed genetic algorithm, in conjunction with discrete wavelets, for carrying out successful forecasts of the trend in financial time series, that includes the NASDAQ composite index. Discrete wavelets isolate the local, small scale variations in these non-stationary time series, after which th…

The article presents methods to select models from behavioral learning data, with applications to contextual bandits.

A defining feature of non-stationary systems is the time dependence of their statistical parameters. Measured time series may exhibit Gaussian statistics on short time horizons, due to the central limit theorem. The sample statistics for long time horizons, however, averages over the time-dependent parameters. To model…

New algorithm tackles non-stationary delayed feedback in recommender systems.

Incremental learning from non-stationary data poses special challenges to the field of machine learning. Although new algorithms have been developed for this, assessment of results and comparison of behaviors are still open problems, mainly because evaluation metrics, adapted from more traditional tasks, can be ineffec…

Study on fake stationary Volterra Heston model for non-stationary processes.

Method learns evolving policies in healthcare contexts.

We apply a recently developed wavelet based approach to characterize the correlation and scaling properties of non-stationary financial time series. This approach is local in nature and it makes use of wavelets from the Daubechies family for detrending purpose. The built-in variable windows in wavelet transform makes t…

This paper proposes a hierarchical feature extractor for non-stationary streaming time series based on the concept of switching observable Markov chain models. The slow time-scale non-stationary behaviors are considered to be a mixture of quasi-stationary fast time-scale segments that are exhibited by complex dynamical…

ABO extends RLS for online learning in non-stationary time-series, improving accuracy and speed.

LC-SAC tackles non-stationary dynamics in reinforcement learning.

The scaling properties of oil price fluctuations are described as a non-stationary stochastic process realized by a time series of finite length. An original model is used to extract the scaling exponent of the fluctuation functions within a non-stationary process formulation. It is shown that, when returns are measure…

OML-AD detects anomalies in non-stationary time series data.

HHAR-net uses neural networks to recognize human activities at different levels of abstraction.

We consider a stochastic linear bandit model in which the available actions correspond to arbitrary context vectors whose associated rewards follow a non-stationary linear regression model. In this setting, the unknown regression parameter is allowed to vary in time. To address this problem, we propose D-LinUCB, a nove…

We consider random vectors drawn from a multivariate normal distribution and compute the sample statistics in the presence of non-stationary correlations. For this purpose, we construct an ensemble of random correlation matrices and average the normal distribution over this ensemble. The resulting distribution contains…

We discuss the statistical properties of index returns in a financial market just after a major market crash. The observed non-stationary behavior of index returns is characterized in terms of the exceedances over a given threshold. This characterization is analogous to the Omori law originally observed in geophysics. …

New algorithms for GLMs adapt to non-stationary contexts.

Observer learns optimal policy from learner's actions without rewards.

New definition resolves ambiguity in non-stationary bandit classification.

To understand the structural dynamics of a large-scale social, biological or technological network, it may be useful to discover behavioral roles representing the main connectivity patterns present over time. In this paper, we propose a scalable non-parametric approach to automatically learn the structural dynamics of …

Improved Adam for time series forecasting with distributional drift.

Master algorithm fails to detect non-stationarity in practical settings.

In this paper we propose a new model for volatility fluctuations in financial time series. This model relies on a non-stationary gaussian process that exhibits aging behavior. It turns out that its properties, over any finite time interval, are very close to continuous cascade models. These latter models are indeed wel…

SmoothFBO tackles non-stationary functional bilevel optimization.

Proposes a new algorithm for non-stationary bandits.

Study incentivizes exploration in non-stationary MAB with compensation.

New algorithm tackles non-stationary reinforcement learning with general function approximation.

Proposes a method to forecast non-stationary time series.

New findings show Bregman proximal algorithms can get stuck near non-stationary points.

New definitions of ESP for quantum reservoir computing handle non-stationary systems.

Study combines VICReg and TNC for better encoding of non-stationary seismic signals.

We propose non-stationary spectral kernels for Gaussian process regression. We propose to model the spectral density of a non-stationary kernel function as a mixture of input-dependent Gaussian process frequency density surfaces. We solve the generalised Fourier transform with such a model, and present a family of non-…

Algorithm adapts to non-stationary rewards without prior knowledge.

This paper develops the first method for the exact simulation of reflected Brownian motion (RBM) with non-stationary drift and infinitesimal variance. The running time of generating exact samples of non-stationary RBM at any time $t$ is uniformly bounded by $\mathcal{O}(1/\barγ^2)$ where $\barγ$ is the average drift of…

Unified review of methods for inferring non-stationary process parameters.

Unified approach for non-stationary and clustered bandits.

In this paper, we obtain the finite-horizon and infinite-horizon ruin probability asymptotics for risk processes with claims of subexponential tails for non-stationary arrival processes that satisfy a large deviation principle. As a result, the arrival process can be dependent, non-stationary and non-renewal. We give t…

Non-stationarity appears in many online applications such as web search and advertising. In this paper, we study the online learning to rank problem in a non-stationary environment where user preferences change abruptly at an unknown moment in time. We consider the problem of identifying the K most attractive items and…

Develops a deep non-stationary kernel for non-stationary spatio-temporal point processes.

Bayesian model predicts interest rates with short-term accuracy and long-term stability.

Paper tackles non-stationary kernelized bandits with near-optimal algorithm.

The paper provides exact multivariate amplitude distributions for non-stationary Gaussian or algebraic fluctuations.

Efficient GP framework for scalable non-stationary processes.

Imitation learning algorithms can be used to learn a policy from expert demonstrations without access to a reward signal. However, most existing approaches are not applicable in multi-agent settings due to the existence of multiple (Nash) equilibria and non-stationary environments. We propose a new framework for multi-…

Flexible GP model improves wind power prediction accuracy.