368 citations · 449 across the 18 of their papers we have counts for
7 papers · 1 filter
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…