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.
Bayesian approach detects changepoints with cost-sensitive data fidelity.
Identifying changes in the generative process of sequential data, known as changepoint detection, has become an increasingly important topic for a wide variety of fields. A recently developed approach, which we call EXact Online Bayesian Changepoint Detection (EXO), has shown reasonable results with efficient computati…
Changepoints are abrupt variations in the generative parameters of a data sequence. Online detection of changepoints is useful in modelling and prediction of time series in application areas such as finance, biometrics, and robotics. While frequentist methods have yielded online filtering and prediction techniques, mos…
Paper introduces a fast, robust, scalable method for detecting changes in data streams.
A new framework detects changepoints in complex data.
Bayesian online changepoint detection (BOCPD) (Adams & MacKay, 2007) offers a rigorous and viable way to identify changepoints in complex systems. In this work, we introduce a Stein variational online changepoint detection (SVOCD) method to provide a computationally tractable generalization of BOCPD beyond the exponent…
Bayesian On-line Changepoint Detection is extended to on-line model selection and non-stationary spatio-temporal processes. We propose spatially structured Vector Autoregressions (VARs) for modelling the process between changepoints (CPs) and give an upper bound on the approximation error of such models. The resulting …
We consider Bayesian analysis of a class of multiple changepoint models. While there are a variety of efficient ways to analyse these models if the parameters associated with each segment are independent, there are few general approaches for models where the parameters are dependent. Under the assumption that the depen…
PITMonitor monitors model calibration over time with formal error guarantees.
We present the very first robust Bayesian Online Changepoint Detection algorithm through General Bayesian Inference (GBI) with -divergences. The resulting inference procedure is doubly robust for both the parameter and the changepoint (CP) posterior, with linear time and constant space complexity. We provide a const…
This study benchmarks changepoint detection algorithms on cardiac time series data.
Detects potential depegs in Curve's StableSwap pools to protect LPs.
New algorithm for precise changepoint localization without assumptions.
Identifying changes in model parameters is fundamental in machine learning and statistics. However, standard changepoint models are limited in expressiveness, often addressing unidimensional problems and assuming instantaneous changes. We introduce change surfaces as a multidimensional and highly expressive generalizat…
Detects changes in topic proportions over time in large text datasets.
We present an algorithm for marginalising changepoints in time-series models that assume a fixed number of unknown changepoints. Our algorithm is differentiable with respect to its inputs, which are the values of latent random variables other than changepoints. Also, it runs in time O(mn) where n is the number of time …
New exact tests detect changepoints in binary and count data, especially when normal approximations fail.
Proposes using MLP for predicting optimal penalty in changepoint detection.
Unified theoretical guarantees for distribution-free changepoint detection and testing.
New method detects changepoints in PDEs using optimized neural networks.
Fast detection of changepoints in linear regression models.
This paper offers a distribution-free method for post-detection changepoint localization.
The objective of the change-point detection is to discover the abrupt property changes lying behind the time-series data. In this paper, we firstly summarize the definition and in-depth implication of the changepoint detection. The next stage is to elaborate traditional and some alternative model-based changepoint dete…
Meta-learning is a promising strategy for learning to efficiently learn within new tasks, using data gathered from a distribution of tasks. However, the meta-learning literature thus far has focused on the task segmented setting, where at train-time, offline data is assumed to be split according to the underlying task,…
Many real-world time series, such as in health, have changepoints where the system's structure or parameters change. Since changepoints can indicate critical events such as onset of illness, it is highly important to detect them. However, existing methods for changepoint detection (CPD) often require user-specified mod…
Develops methods for inference after detecting a change in sequential data.
Change detection (CD) in time series data is a critical problem as it reveal changes in the underlying generative processes driving the time series. Despite having received significant attention, one important unexplored aspect is how to efficiently utilize additional correlated information to improve the detection and…
We consider the setup of stochastic multi-armed bandits in the case when reward distributions are piecewise i.i.d. and bounded with unknown changepoints. We focus on the case when changes happen simultaneously on all arms, and in stark contrast with the existing literature, we target gap-dependent (as opposed to only g…
In the multiple changepoint setting, various search methods have been proposed which involve optimising either a constrained or penalised cost function over possible numbers and locations of changepoints using dynamic programming. Such methods are typically computationally intensive. Recent work in the penalised optimi…
Study on network-valued processes with asynchronous updates, proving consistency in community and changepoint estimation.
A new algorithm detects changepoints in labeled and unlabeled data.
Many traditional methods for identifying changepoints can struggle in the presence of outliers, or when the noise is heavy-tailed. Often they will infer additional changepoints in order to fit the outliers. To overcome this problem, data often needs to be pre-processed to remove outliers, though this is difficult for a…
A flexible nonparametric online changepoint detection algorithm for high-frequency data.
Change detection involves segmenting sequential data such that observations in the same segment share some desired properties. Multivariate change detection continues to be a challenging problem due to the variety of ways change points can be correlated across channels and the potentially poor signal-to-noise ratio on …
Improved trading strategy using deep learning and changepoint detection for market changes.
Non-parametric estimators improve quickest changepoint detection under irregular sequence lengths.
Improved online changepoint detection for autocorrelated data.
New optimization method improves AUC for binary classification and changepoint detection.
New algorithm optimizes AUC in binary classification and changepoint detection.
Changepoint detection is a central problem in time series and genomic data. For some applications, it is natural to impose constraints on the directions of changes. One example is ChIP-seq data, for which adding an up-down constraint improves peak detection accuracy, but makes the optimization problem more complicated.…
The time-evolving precision matrix of a piecewise-constant Gaussian graphical model encodes the dynamic conditional dependency structure of a multivariate time-series. Traditionally, graphical models are estimated under the assumption that data is drawn identically from a generating distribution. Introducing sparsity a…
A new algorithm detects changes in data with constant cost per iteration.
Detects changes in classifier scores to identify shifts in class priors.
Novel graph-based method detects R-peaks in noisy ECG signals without preprocessing.
FOCuS detects changes in mean from high-frequency data efficiently.
New method detects changes in high-dimensional Gaussian data streams.
Optimizes sensor usage for detecting abrupt changes in sensor data.
Inference over tails is usually performed by fitting an appropriate limiting distribution over observations that exceed a fixed threshold. However, the choice of such threshold is critical and can affect the inferential results. Extreme value mixture models have been defined to estimate the threshold using the full dat…