368 citations · 516 across the 9 of their papers we have counts for
9 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…
fev-bench: A Realistic Benchmark for Time Series Forecasting
Oleksandr Shchur, Abdul Fatir Ansari, Caner Turkmen +5
Benchmark quality is critical for meaningful evaluation and sustained progress in time series forecasting, particularly with the rise of pretrained models. Existing benchmarks ofte…
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…
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…
Anomaly Detection at Scale: The Case for Deep Distributional Time Series Models
Fadhel Ayed, Lorenzo Stella, Tim Januschowski +1
This paper introduces a new methodology for detecting anomalies in time series data, with a primary application to monitoring the health of (micro-) services and cloud resources. T…
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…