368 citations · 679 across the 19 of their papers we have counts for
19 papers
Chronos-2: From Univariate to Universal Forecasting
Abdul Fatir Ansari, Oleksandr Shchur, Jaris Küken +20
Pretrained time series models have enabled inference-only forecasting systems that produce accurate predictions without task-specific training. However, existing approaches largely…
Zero-Shot Time Series Forecasting with Covariates via In-Context Learning
Andreas Auer, Raghul Parthipan, Pedro Mercado +5
Pretrained time series models, capable of zero-shot forecasting, have demonstrated significant potential in enhancing both the performance and accessibility of time series forecast…
ChronosX: Adapting Pretrained Time Series Models with Exogenous Variables
Sebastian Pineda Arango, Pedro Mercado, Shubham Kapoor +10
Covariates provide valuable information on external factors that influence time series and are critical in many real-world time series forecasting tasks. For example, in retail, co…
Enhancing Foundation Models for Time Series Forecasting via Wavelet-based Tokenization
Luca Masserano, Abdul Fatir Ansari, Boran Han +8
How to best develop foundational models for time series forecasting remains an important open question. Tokenization is a crucial consideration in this effort: what is an effective…
Chronos: Learning the Language of Time Series
Abdul Fatir Ansari, Lorenzo Stella, Caner Turkmen +15
We introduce Chronos, a simple yet effective framework for pretrained probabilistic time series models. Chronos tokenizes time series values using scaling and quantization into a f…
Deep Non-Parametric Time Series Forecaster
Syama Sundar Rangapuram, Jan Gasthaus, Lorenzo Stella +4
This paper presents non-parametric baseline models for time series forecasting. Unlike classical forecasting models, the proposed approach does not assume any parametric form for t…
Adaptive Sampling for Probabilistic Forecasting under Distribution Shift
Luca Masserano, Syama Sundar Rangapuram, Shubham Kapoor +3
The world is not static: This causes real-world time series to change over time through external, and potentially disruptive, events such as macroeconomic cycles or the COVID-19 pa…
Solving Recurrent MIPs with Semi-supervised Graph Neural Networks
Konstantinos Benidis, Ugo Rosolia, Syama Rangapuram +2
We propose an ML-based model that automates and expedites the solution of MIPs by predicting the values of variables. Our approach is motivated by the observation that many problem…
Multivariate Time Series Forecasting with Latent Graph Inference
Victor Garcia Satorras, Syama Sundar Rangapuram, Tim Januschowski
This paper introduces a new approach for Multivariate Time Series forecasting that jointly infers and leverages relations among time series. Its modularity allows it to be integrat…
Neural Flows: Efficient Alternative to Neural ODEs
Marin Biloš, Johanna Sommer, Syama Sundar Rangapuram +2
Neural ordinary differential equations describe how values change in time. This is the reason why they gained importance in modeling sequential data, especially when the observatio…
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…
GluonTS: Probabilistic Time Series Models in Python
Alexander Alexandrov, Konstantinos Benidis, Michael Bohlke-Schneider +10
We introduce Gluon Time Series (GluonTS, available at https://gluon-ts.mxnet.io), a library for deep-learning-based time series modeling. GluonTS simplifies the development of and…
Approximate Bayesian Inference in Linear State Space Models for Intermittent Demand Forecasting at Scale
Matthias Seeger, Syama Rangapuram, Yuyang Wang +4
We present a scalable and robust Bayesian inference method for linear state space models. The method is applied to demand forecasting in the context of a large e-commerce platform,…
Methods for Sparse and Low-Rank Recovery under Simplex Constraints
Ping Li, Syama Sundar Rangapuram, Martin Slawski
The de-facto standard approach of promoting sparsity by means of -regularization becomes ineffective in the presence of simplex constraints, i.e.,~the target is known to ha…
Towards Realistic Team Formation in Social Networks based on Densest Subgraphs
Syama Sundar Rangapuram, Thomas Bühler, Matthias Hein
Given a task , a set of experts with multiple skills and a social network reflecting the compatibility among the experts, team formation is the problem o…
Constrained 1-Spectral Clustering
Syama Sundar Rangapuram, Matthias Hein
An important form of prior information in clustering comes in form of cannot-link and must-link constraints. We present a generalization of the popular spectral clustering techniqu…
Tight Continuous Relaxation of the Balanced -Cut Problem
Syama Sundar Rangapuram, Pramod Kaushik Mudrakarta, Matthias Hein
Spectral Clustering as a relaxation of the normalized/ratio cut has become one of the standard graph-based clustering methods. Existing methods for the computation of multiple clus…
The Total Variation on Hypergraphs - Learning on Hypergraphs Revisited
Matthias Hein, Simon Setzer, Leonardo Jost +1
Hypergraphs allow one to encode higher-order relationships in data and are thus a very flexible modeling tool. Current learning methods are either based on approximations of the hy…
Constrained fractional set programs and their application in local clustering and community detection
Thomas Bühler, Syama Sundar Rangapuram, Simon Setzer +1
The (constrained) minimization of a ratio of set functions is a problem frequently occurring in clustering and community detection. As these optimization problems are typically NP-…