A locally-built, LLM-digested index of recent arXiv papers in quant finance, geometry/topology, and statistical ML — keyword search served straight from SQLite on this machine.
Estimates time-varying network connections using multi-stage smoothing.
A new method for embedding temporal relationships in graphs.
TATD predicts missing entries in time-evolving tensors by exploiting temporal dependency and sparsity.
We present a principled approach for detecting overlapping temporal community structure in dynamic networks. Our method is based on the following framework: find the overlapping temporal community structure that maximizes a quality function associated with each snapshot of the network subject to a temporal smoothness c…
In a dynamic network, the neighborhood of the vertices evolve across different temporal snapshots of the network. Accurate modeling of this temporal evolution can help solve complex tasks involving real-life social and interaction networks. However, existing models for learning latent representation are inadequate for …
Paper introduces an unsupervised tensor-based anomaly detection method for spatiotemporal data.
Quantile Temporal-Difference learning proved convergent with proof.
Evolutionary clustering aims at capturing the temporal evolution of clusters. This issue is particularly important in the context of social media data that are naturally temporally driven. In this paper, we propose a new probabilistic model-based evolutionary clustering technique. The Temporal Multinomial Mixture (TMM)…
Post-estimation smoothing improves prediction accuracy with structural indices.
Recent works demonstrated the usefulness of temporal coherence to regularize supervised training or to learn invariant features with deep architectures. In particular, enforcing smooth output changes while presenting temporally-closed frames from video sequences, proved to be an effective strategy. In this paper we pro…
Unified framework for efficient Gaussian process inference.
Proposes tPARAFAC2 for tracking evolving patterns in time-evolving data.
Efficient spatio-temporal Gaussian process inference method.
Geometric method captures rare topics and temporal alignment in co-author networks.
New analysis shows how temporal variability affects online learning performance.
The Temporal Group LASSO is an example of a multi-task, regularized regression approach for the prediction of response variables that vary over time. The aim of this work is to introduce the reader to the concepts behind the Temporal Group LASSO and its related methods, as well as to the type of potential applications …
Predictive process monitoring is concerned with the analysis of events produced during the execution of a business process in order to predict as early as possible the final outcome of an ongoing case. Traditionally, predictive process monitoring methods are optimized with respect to accuracy. However, in environments …
Several applications of Reinforcement Learning suffer from instability due to high variance. This is especially prevalent in high dimensional domains. Regularization is a commonly used technique in machine learning to reduce variance, at the cost of introducing some bias. Most existing regularization techniques focus o…
PARAFAC2 has demonstrated success in modeling irregular tensors, where the tensor dimensions vary across one of the modes. An example scenario is modeling treatments across a set of patients with the varying number of medical encounters over time. Despite recent improvements on unconstrained PARAFAC2, its model factors…
Comparing data defined over space and time is notoriously hard, because it involves quantifying both spatial and temporal variability, while at the same time taking into account the chronological structure of data. Dynamic Time Warping (DTW) computes an optimal alignment between time series in agreement with the chrono…
ARM improves multivariate time series forecasting by better capturing series-wise relationships.
New null distance bounds confirm Big Bang singularity in cosmological models.
dCMF models evolving patterns in multiway data with temporal dynamics.
FLUID uses flows to unify filtering and smoothing for complex systems.
The paper proposes a new method for clustering survival data using smoothed log-hazard trajectories.
Solutions to the wave equation on de Sitter-Schwarzschild space with smooth initial data on a Cauchy surface are shown to decay exponentially to a constant at temporal infinity, with corresponding uniform decay on the appropriately compactified space.
Geroch's theorem about the splitting of globally hyperbolic spacetimes is a central result in global Lorentzian Geometry. Nevertheless, this result was obtained at a topological level, and the possibility to obtain a metric (or, at least, smooth) version has been controversial since its publication in 1970. In fact, th…
Proposes DILATE and STRIPE++ for precise time series forecasting.
A new framework for averaging spatio-temporal signals using optimal transport and soft alignments.
Develops state-space deep Gaussian processes for irregular signals.
Unified model forecasts epidemics with spatial and temporal dynamics.
TNC learns time series representations by leveraging temporal neighborhoods.
Stochastic networks are a plausible representation of the relational information among entities in dynamic systems such as living cells or social communities. While there is a rich literature in estimating a static or temporally invariant network from observation data, little has been done toward estimating time-varyin…
This paper proposes a method for machine learning from unlabeled data in the form of a time-series. The mapping that is learned is shown to extract slowly evolving information that would be useful for control applications, while efficiently filtering out unwanted, higher-frequency noise. The method consists of training…
LVTINO improves high-definition video restoration with consistent temporal details.
A simple GI loss improves temporal generalization without complex methods.
CT-OT Flow estimates continuous-time dynamics from discrete snapshots.
Neuroscientific studies of drawing-like movements usually analyze neural representation of either geometric (eg. direction, shape) or temporal (eg. speed) features of trajectories rather than trajectory's representation as a whole. This work is about empirically supported mathematical ideas behind splitting and merging…
New method estimates treatment effects over time for survival data, improving accuracy and smoothness.
We propose and analyze a novel framework for learning sparse representations, based on two statistical techniques: kernel smoothing and marginal regression. The proposed approach provides a flexible framework for incorporating feature similarity or temporal information present in data sets, via non-parametric kernel sm…
Spatio-temporal point process models play a central role in the analysis of spatially distributed systems in several disciplines. Yet, scalable inference remains computa- tionally challenging both due to the high resolution modelling generally required and the analytically intractable likelihood function. Here, we expl…
KOMET identifies Koopman operators from model parameter trajectories to adapt to evolving data distributions.
This paper proposes a new algorithm for learning guidance rewards in RL.
Next-generation sequencing (NGS) to profile temporal changes in living systems is gaining more attention for deriving better insights into the underlying biological mechanisms compared to traditional static sequencing experiments. Nonetheless, the majority of existing statistical tools for analyzing NGS data lack the c…
Hybrid model improves geopolitical conflict forecasting.
Time-varying mixture densities occur in many scenarios, for example, the distributions of keywords that appear in publications may evolve from year to year, video frame features associated with multiple targets may evolve in a sequence. Any models that realistically cater to this phenomenon must exhibit two important p…
The paper develops a new method for estimating non-parametric regression functions with spatio-temporal dependencies.
A new method for filling in missing traffic data improves accuracy over existing techniques.