Paper benchmarks machine learning for detecting process curve drifts.
problem Detecting drifts in multivariate manufacturing process data.
method Synthetic data generation and evaluation score introduction.
result Existing algorithms often fail with complex drift scenarios.
CONDA-PM framework helps analyze concept drift in business processes.
problem Analyzing changes in business processes over time.
method Systematic Literature Review and framework development.
result Highlights areas needing research to complement existing efforts.
New method improves prediction accuracy in business process mining by handling concept drift.
problem Improving prediction quality in business process mining affected by concept drift.
method Systematically analyzed and compared different data selection strategies for retraining machine learning models.
result Improved accuracy from 0.5400 to 0.7010 with concept drift handling.
The paper develops a neural network-based classifier for diffusion process drifts.
problem Classifying diffusion processes with distinct drift functions from discrete observations.
method Derives a Bayes rule and constructs a plug-in classifier using neural networks to estimate drifts.
result Establishes convergence rates for misclassification risk, highlighting benefits of diffusion structure.
The paper studies how expert opinions improve stock return predictions in a market with a hidden drift.
problem Improving stock return predictions in a market with a hidden Gaussian drift.
method Uses Kalman filter techniques to estimate the hidden drift from noisy expert opinions and stock returns.
result The Kalman filter estimates of the drift converge to the hidden drift as the frequency of expert opinions increases.
Study optimizes financial strategies in markets with uncertain drift.
problem Optimizing portfolios in markets with unpredictable drift.
method Combines worst-case optimization with filtering techniques to define uncertainty sets.
result Proves minimax theorem and derives optimal strategies for continuous updates.
New method decomposes portfolio returns into generating and trading processes.
problem Decompose portfolio returns for arbitrary stock portfolios.
method Fisk-Stratonovich integration to extend decomposition to arbitrary portfolios.
result Portfolio returns can be represented by a structural process and trading process.
The paper develops a neural network method for estimating drift functions of diffusion processes from discrete observations.
problem Nonparametric estimation of drift function for diffusion processes from high-frequency discrete observations.
method Neural network-based estimator for drift function estimation.
result Derives a non-asymptotic convergence rate for the neural network estimator.
The paper optimizes portfolios in a market with hidden drift and random expert opinions.
problem Optimizing portfolios in a market with hidden Gaussian drift and random expert signals.
method Modeling the hidden drift using Kalman filters and solving the utility maximization problem with dynamic programming.
result Derivation of optimal portfolio weights and utility maximization under the given market conditions.
The paper examines utility maximization in markets with hidden Gaussian drift, finding restrictions on model parameters.
problem Utility maximization problems in markets with hidden Gaussian drift mean-reverting processes.
method Derives sufficient conditions for bounded maximum expected utility of terminal wealth for models with full and partial information.
result Restrictions on model parameters for bounded maximum expected utility.
A drift detection method for large datasets without labels.
problem Early detection of concept drift in large, unlabeled datasets.
method Classical statistical process control in a label-less setting.
result Better statistical power than previous methods under computational constraints.
Formula found for ruin probabilities in divided insurance companies.
problem Ruin probabilities in divided insurance companies with Lévy processes.
method Formula for supremum distribution of Lévy processes with broken drift.
result Formulas for ruin probabilities in specified proportions.
Paper proves convergence of Markovian iteration for FBSDEs with fully coupled drift and Z process.
problem Proving convergence of Markovian iteration for FBSDEs with fully coupled drift and Z process.
method Differentiation-based approach to handle Z process, uniformly controlling Lipschitz continuity of decoupling fields.
result Proves convergence of Markovian iteration method for FBSDEs with fully coupled drift and Z process.
Bayesian non-parametric model adapts to concept drifts in streaming data.
problem Inference under concept drift phenomenon for non-stationary data streams.
method Variational inference algorithm for Dirichlet process mixture models with exponential forgetting.
result The proposed model outperforms state-of-the-art algorithms in clustering problems.
Geodesic walks converge to Brownian motion on Finsler manifolds.
problem Understanding random walks on Finsler manifolds.
method Analyzing convergence of geodesic random walks to diffusion processes.
result The Brownian motion on a Riemannian metric is a key result.
Paper proposes SGP-Q for efficient online anomaly detection with concept drift adaptation.
problem Efficient online anomaly detection for time-series data with concept drift.
method Sparse Gaussian processes with Q-function (SGP-Q).
result SGP-Q achieves better anomaly detection results with concept drift adaptation.
Researchers infer the drift of a high-dimensional OU process with row-sparsity.
problem Inferring the drift parameter of a high-dimensional Ornstein-Uhlenbeck process under row-sparsity.
method Negative log-likelihood penalized by an ℓ1-penalization (Lasso and Adaptive Lasso). result Sharp oracle inequality and asymptotic consistency for variable selection.
Algorithm detects concept drift and adapts models in streaming data.
problem Concept drift in streaming data renders models inaccurate.
method Adaptive learning algorithm that detects drifts and reacts to them.
result Risk competitive to an algorithm with perfect drift knowledge.
A new drift detection method based on autoregressive models.
problem Concept drift in real-world data leads to decreased model performance.
method Autoregressive based drift detection method (ADDM).
result ADDM outperforms state-of-the-art drift detection methods.
New research shows some distributions hard to sample via diffusions.
problem Some distributions hard to sample via diffusions.
method Learning drifts of diffusions to approximate target distributions.
result Superpolynomially close drifts can yield very far approximations.
By appealing to renewal theory we determine the equations that the mean exit time of a continuous-time random walk with drift satisfies both when the present coincides with a jump instant or when it does not. Particular attention is paid to the corrections ensuing from the non-Markovian nature of the process. We show t…
Paper studies long-run risk optimization with dyadic impulses for unbounded processes.
problem Long-run risk optimization problem with unbounded and non-uniformly ergodic processes.
method Adapting weight norm approach, combining geometric drift and local minorization property.
result Existence of solution to Bellman equation for risk-averse parameters.
The paper studies expert opinions in financial markets using diffusion approximations.
problem Estimating hidden drift in financial markets with expert opinions.
method Investigates asymptotic behavior of filter for high-frequency expert opinions, derives diffusion approximations.
result Expert opinions can be approximated by a diffusion process, simplifying utility maximization problems.
Investors maximize utility from power functions with non-Markovian processes.
problem Optimal investment with processes of long or negative memory.
method Frechet-differentiability, Banach space, fractional Brownian motion, asymptotic results.
result First order expansions and maximal utility achievable in certain problems.
Detects drifts in data for classification tasks using constrained embeddings.
problem Drifts in data affect model performance; unsupervised methods ignore label information.
method Task-sensitive semi-supervised drift detection with constrained low-dimensional embedding.
result Successfully detects real drifts affecting classification performance.
AMUSE uses reinforcement learning to predict optimal model updates.
problem Concept drift weakens model performance over time.
method Reinforcement learning in a simulated environment.
result AMUSE proactively recommends updates based on performance improvements.
We study a problem of finding an optimal stopping strategy to liquidate an asset with unknown drift. Taking a Bayesian approach, we model the initial beliefs of an individual about the drift parameter by allowing an arbitrary probability distribution to characterise the uncertainty about the drift parameter. Filtering …
Auto-ML framework tackles data drift using GBDT and incremental learning.
problem Data distribution changes over time, reducing model performance.
method GBDT and Incremental learning for handling data drift.
result Effective solution for data drift with robust performance.
The existence of the pricing kernel is shown to imply the existence of an ambient information process that generates market filtration. This information process consists of a signal component concerning the value of the random variable X that can be interpreted as the timing of future cash demand, and an independent no…
The paper optimizes portfolios using MACD signals derived from price history.
problem Optimizing risky asset portfolios with latent mean-reverting and momentum factors.
method Derives optimal strategies based on MACD signals from EMA processes.
result Establishes admissibility and verification of optimal strategies.
Estimates SDEs from sparse data using Gaussian processes.
problem Estimating drift and diffusion functions in SDEs from sparse observations.
method Gaussian processes for flexible modeling, approximate EM algorithm, sparse Gaussian process approximation.
result Accurate estimation of SDE parameters from sparse data.
Study optimal trading strategies with expert signals in a hidden Gaussian drift market.
problem Optimal trading strategies in a financial market with hidden Gaussian drift and expert signals.
method Transformed power utility maximization problem into full information problem using Kalman filter estimates of the drift.
result Closed-form solutions for value function and optimal trading strategy derived.
Study shows physical drift affects put-call parity enforcement, not just option payoffs.
problem Inconsistency between quoted put-call parity and actual market behavior.
method Examined SPX and RUT index options, used drift-preserving GBM term to improve fit.
result Physical drift enters the enforcement of risk-neutral parity, not just option payoffs.
Method learns SDEs from data snapshots.
problem Learning drift and diffusion of SDEs from data.
method Two-step process: learn drift by expected value, learn diffusion by SDP.
result Validated on examples and simulations.
New algorithm detects concept drifts from unlabeled data streams.
problem Detecting concept drifts from unlabeled data streams.
method Margin Density Drift Detection (MD3) algorithm.
result MD3 reliably detects drifts with fewer false alarms.
The maximum likelihood approach is adapted to the problem of estimation of drift and diffusion functions of stochastic processes from measured time series. We reconcile a previously devised iterative procedure [Kleinhans et al., Physics Letters A (346), 2005] and put the application of the method on a firm theoretical …
We discuss a simple extension of the Ho and Lee model with generic time-dependent drift in which: 1) we compute bond prices analytically; 2) the yield curve is sensible and the asymptotic yield is positive; and 3) our analytical solution provides a clean and simple way of separating volatility from the drift in the sho…
Study improves survival analysis for credit risk by accounting for data drift.
problem Survival analysis in credit risk assumes a stationary data-generating process, but real-world data drift affects model performance.
method Proposes a dynamic joint modelling framework integrating longitudinal behavioural markers and hazard formulations, combined with drift-adaptive techniques.
result Proposed model outperforms classical survival models and drift-adaptive learners in various data drift scenarios.
We discuss martingales, detrending data, and the efficient market hypothesis for stochastic processes x(t) with arbitrary diffusion coefficients D(x,t). Beginning with x-independent drift coefficients R(t) we show that Martingale stochastic processes generate uncorrelated, generally nonstationary increments. Generally,…
This research generates synthetic data streams for handling concept drifts and novel classes.
problem Handling concept drifts and novel classes in dynamic data streams.
method Synthetic data stream generation for both concept drifts and novel classes.
result Demonstrates the effectiveness of unsupervised drift detectors in open set recognition.
New method calculates geometric Brownian motion with affine drift and its integral.
problem Calculating the distribution of geometric Brownian motion with affine drift and its integral.
method Laplace transform approach and Heun differential equation.
result Joint distribution of geometric Brownian motion with affine drift and its integral can be determined.
Framework models supervised learning in non-stationary data.
problem Non-stationary data in supervised learning.
method Statistical physics methods applied to LVQ and neural networks.
result LVQ and ReLU have different sensitivity to concept drift.
Reduces test set maintenance effort by 80-100%.
problem Lack of proper and up-to-date test sets in real-world scenarios.
method Simple technique to reduce labeling effort.
result Significant reduction in test set maintenance effort (80-100%).
We calculate moments of decision times for diffusion models.
problem Modeling decision times in cognitive tasks.
method Derive expressions for the first three moments of decision times.
result Explicit formulae for third and higher moments are provided.
LUNAR uses cellular automata for real-time data classification in fast streams.
problem Real-time machine learning challenges with fast data streams and concept drift.
method Streamified cellular automata approach for incremental learning and adaptation.
result Competitive performance in classification compared to established online learning methods.
A framework for prototype-based classifiers in changing data environments.
problem Learning in non-stationary environments with concept drift.
method Analytical methods from statistical physics applied to LVQ systems.
result Basic LVQ algorithms are suitable for non-stationary environments, but weight decay does not improve performance.
This paper investigates optimal portfolio strategies in a financial market where the drift of the stock returns is driven by an unobserved Gaussian mean reverting process. Information on this process is obtained from observing stock returns and expert opinions. The latter provide at discrete time points an unbiased est…
Efficiently reconstructs jump-diffusion processes from data using neural networks.
problem Reconstructing jump-diffusion processes from data.
method Temporally decoupled squared Wasserstein distance method using parameterized neural networks.
result Enhanced reconstruction of jump-diffusion processes from data.