368 citations · 449 across the 18 of their papers we have counts for
19 papers
REDO: Execution-Free Runtime Error Detection for COding Agents
Shou Li, Andrey Kan, Laurent Callot +3
As LLM-based agents exhibit exceptional capabilities in addressing complex problems, there is a growing focus on developing coding agents to tackle increasingly sophisticated tasks…
MELODY: Robust Semi-Supervised Hybrid Model for Entity-Level Online Anomaly Detection with Multivariate Time Series
Jingchao Ni, Gauthier Guinet, Peihong Jiang +2
In large IT systems, software deployment is a crucial process in online services as their code is regularly updated. However, a faulty code change may degrade the target service's…
Criteria for Classifying Forecasting Methods
Tim Januschowski, Jan Gasthaus, Yuyang Wang +4
Classifying forecasting methods as being either of a "machine learning" or "statistical" nature has become commonplace in parts of the forecasting literature and community, as exem…
SpectraNet: Multivariate Forecasting and Imputation under Distribution Shifts and Missing Data
Cristian Challu, Peihong Jiang, Ying Nian Wu +1
In this work, we tackle two widespread challenges in real applications for time-series forecasting that have been largely understudied: distribution shifts and missing data. We pro…
Unsupervised Model Selection for Time-series Anomaly Detection
Mononito Goswami, Cristian Challu, Laurent Callot +2
Anomaly detection in time-series has a wide range of practical applications. While numerous anomaly detection methods have been proposed in the literature, a recent survey conclude…
Robust Projection based Anomaly Extraction (RPE) in Univariate Time-Series
Mostafa Rahmani, Anoop Deoras, Laurent Callot
This paper presents a novel, closed-form, and data/computation efficient online anomaly detection algorithm for time-series data. The proposed method, dubbed RPE, is a window-based…
Deep Generative model with Hierarchical Latent Factors for Time Series Anomaly Detection
Cristian Challu, Peihong Jiang, Ying Nian Wu +1
Multivariate time series anomaly detection has become an active area of research in recent years, with Deep Learning models outperforming previous approaches on benchmark datasets.…
Testing Granger Non-Causality in Panels with Cross-Sectional Dependencies
Lenon Minorics, Caner Turkmen, David Kernert +3
This paper proposes a new approach for testing Granger non-causality on panel data. Instead of aggregating panel member statistics, we aggregate their corresponding p-values and sh…
Online Time Series Anomaly Detection with State Space Gaussian Processes
Christian Bock, François-Xavier Aubet, Jan Gasthaus +3
We propose r-ssGPFA, an unsupervised online anomaly detection model for uni- and multivariate time series building on the efficient state space formulation of Gaussian processes. F…
Online false discovery rate control for anomaly detection in time series
Quentin Rebjock, Barış Kurt, Tim Januschowski +1
This article proposes novel rules for false discovery rate control (FDRC) geared towards online anomaly detection in time series. Online FDRC rules allow to control the properties…
Spliced Binned-Pareto Distribution for Robust Modeling of Heavy-tailed Time Series
Elena Ehrlich, Laurent Callot, François-Xavier Aubet
This work proposes a novel method to robustly and accurately model time series with heavy-tailed noise, in non-stationary scenarios. In many practical application time series have…
Improve black-box sequential anomaly detector relevancy with limited user feedback
Luyang Kong, Lifan Chen, Ming Chen +2
Anomaly detectors are often designed to catch statistical anomalies. End-users typically do not have interest in all of the detected outliers, but only those relevant to their appl…
A simple and effective predictive resource scaling heuristic for large-scale cloud applications
Valentin Flunkert, Quentin Rebjock, Joel Castellon +2
We propose a simple yet effective policy for the predictive auto-scaling of horizontally scalable applications running in cloud environments, where compute resources can only be ad…
Deep Learning for Time Series Forecasting: Tutorial and Literature Survey
Konstantinos Benidis, Syama Sundar Rangapuram, Valentin Flunkert +10
Deep learning based forecasting methods have become the methods of choice in many applications of time series prediction or forecasting often outperforming other approaches. Conseq…
High-Dimensional Multivariate Forecasting with Low-Rank Gaussian Copula Processes
David Salinas, Michael Bohlke-Schneider, Laurent Callot +2
Predicting the dependencies between observations from multiple time series is critical for applications such as anomaly detection, financial risk management, causal analysis, or de…
A Nodewise Regression Approach to Estimating Large Portfolios
Laurent Callot, Mehmet Caner, Esra Ulasan +1
This paper investigates the large sample properties of the variance, weights, and risk of high-dimensional portfolios where the inverse of the covariance matrix of excess asset ret…
Sharp Threshold Detection Based on Sup-norm Error rates in High-dimensional Models
Laurent Callot, Mehmet Caner, Anders Bredahl Kock +1
We propose a new estimator, the thresholded scaled Lasso, in high dimensional threshold regressions. First, we establish an upper bound on the estimation error of the…
Vector Autoregressions with Parsimoniously Time Varying Parameters and an Application to Monetary Policy
Laurent Callot, Johannes Tang Kristensen
This paper proposes a parsimoniously time varying parameter vector autoregressive model (with exogenous variables, VARX) and studies the properties of the Lasso and adaptive Lasso…
Oracle Inequalities for High Dimensional Vector Autoregressions
Anders Bredahl Kock, Laurent A. F. Callot
This paper establishes non-asymptotic oracle inequalities for the prediction error and estimation accuracy of the LASSO in stationary vector autoregressive models. These inequaliti…