259 citations · 347 across the 12 of their papers we have counts for
13 papers
The Nixtlaverse: An Open-Source Ecosystem for Forecasting
Olivier Sprangers, Max Mergenthaler Canseco, Marco Peixeiro +8
Large forecasting applications often combine statistical, machine-learning, and neural models. These families solve the same problem but differ in fitted state, training procedures…
Investigating Compositional Reasoning in Time Series Foundation Models
Willa Potosnak, Cristian Challu, Mononito Goswami +4
Large pre-trained time series foundation models (TSFMs) have demonstrated promising zero-shot performance across a wide range of domains. However, a question remains: Do TSFMs succ…
Implicit Reasoning in Deep Time Series Forecasting
Willa Potosnak, Cristian Challu, Mononito Goswami +3
Recently, time series foundation models have shown promising zero-shot forecasting performance on time series from a wide range of domains. However, it remains unclear whether thei…
TimeGPT-1
Azul Garza, Cristian Challu, Max Mergenthaler-Canseco
In this paper, we introduce TimeGPT, the first foundation model for time series, capable of generating accurate predictions for diverse datasets not seen during training. We evalua…
Global Deep Forecasting with Patient-Specific Pharmacokinetics
Willa Potosnak, Cristian Challu, Kin G. Olivares +2
Forecasting healthcare time series data is vital for early detection of adverse outcomes and patient monitoring. However, it can be challenging in practice due to variable medicati…
Hierarchically Coherent Multivariate Mixture Networks
Kin G. Olivares, David Luo, Cristian Challu +3
Large collections of time series data are often organized into hierarchies with different levels of aggregation; examples include product and geographical groupings. Probabilistic…
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…
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.…
N-HiTS: Neural Hierarchical Interpolation for Time Series Forecasting
Cristian Challu, Kin G. Olivares, Boris N. Oreshkin +3
Recent progress in neural forecasting accelerated improvements in the performance of large-scale forecasting systems. Yet, long-horizon forecasting remains a very difficult task. T…
DMIDAS: Deep Mixed Data Sampling Regression for Long Multi-Horizon Time Series Forecasting
Cristian Challu, Kin G. Olivares, Gus Welter +1
Neural forecasting has shown significant improvements in the accuracy of large-scale systems, yet predicting extremely long horizons remains a challenging task. Two common problems…
Neural basis expansion analysis with exogenous variables: Forecasting electricity prices with NBEATSx
Kin G. Olivares, Cristian Challu, Grzegorz Marcjasz +2
We extend the neural basis expansion analysis (NBEATS) to incorporate exogenous factors. The resulting method, called NBEATSx, improves on a well performing deep learning model, ex…
Double Adaptive Stochastic Gradient Optimization
Kin Gutierrez, Jin Li, Cristian Challu +1
Adaptive moment methods have been remarkably successful in deep learning optimization, particularly in the presence of noisy and/or sparse gradients. We further the advantages of a…