259 citations · 312 across the 13 of their papers we have counts for
15 papers
Scale-Invariant Training for Time Series Foundation Models
Ignacy Stepka, Willa Potosnak, Kin G. Olivares +1
Time series foundation models (TSFMs) are trained on large collections of time series datasets that span various morphologies and domains. This setting exposes models to series who…
Time-Aware Prior Fitted Networks for Zero-Shot Forecasting with Exogenous Variables
Andres Potapczynski, Ravi Kiran Selvam, Tatiana Konstantinova +7
In many time series forecasting settings, the target time series is accompanied by exogenous covariates, such as promotions and prices in retail demand; temperature in energy load;…
Zero-shot Forecasting by Simulation Alone
Boris N. Oreshkin, Mayank Jauhari, Ravi Kiran Selvam +10
Zero-shot time-series forecasting holds great promise, but is still in its infancy, hindered by limited and biased data corpora, leakage-prone evaluation, and privacy and licensing…
A More Realistic Evaluation of Cross-Frequency Transfer Learning and Foundation Forecasting Models
Kin G. Olivares, Malcolm Wolff, Tatiana Konstantinova +8
Cross-frequency transfer learning (CFTL) has emerged as a popular framework for curating large-scale time series datasets to pre-train foundation forecasting models (FFMs). Althoug…
SPADE-S: A Sparsity-Robust Foundational Forecaster
Malcolm Wolff, Matthew Li, Ravi Kiran Selvam +11
Despite significant advancements in time series forecasting, accurate modeling of time series with strong heterogeneity in magnitude and/or sparsity patterns remains challenging fo…
TAT: Temporal-Aligned Transformer for Multi-Horizon Peak Demand Forecasting
Zhiyuan Zhao, Sitan Yang, Kin G. Olivares +5
Multi-horizon time series forecasting has many practical applications such as demand forecasting. Accurate demand prediction is critical to help make buying and inventory decisions…
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…
SPADE Split Peak Attention DEcomposition
Malcolm Wolff, Kin G. Olivares, Boris Oreshkin +8
Demand forecasting faces challenges induced by Peak Events (PEs) corresponding to special periods such as promotions and holidays. Peak events create significant spikes in demand f…
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…
HierarchicalForecast: A Reference Framework for Hierarchical Forecasting in Python
Kin G. Olivares, Azul Garza, David Luo +5
Large collections of time series data are commonly organized into structures with different levels of aggregation; examples include product and geographical groupings. It is often…
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…
Probabilistic Hierarchical Forecasting with Deep Poisson Mixtures
Kin G. Olivares, O. Nganba Meetei, Ruijun Ma +3
Hierarchical forecasting problems arise when time series have a natural group structure, and predictions at multiple levels of aggregation and disaggregation across the groups are…
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…