61 citations · 63 across the 9 of their papers we have counts for
9 papers
The Forecast Critic: Leveraging Large Language Models for Poor Forecast Identification
Luke Bhan, Hanyu Zhang, Andrew Gordon Wilson +2
Monitoring forecasting systems is critical for customer satisfaction, profitability, and operational efficiency in large-scale retail businesses. We propose The Forecast Critic, a…
Understanding the Implicit Biases of Design Choices for Time Series Foundation Models
Annan Yu, Danielle C. Maddix, Boran Han +7
Time series foundation models (TSFMs) are a class of potentially powerful, general-purpose tools for time series forecasting and related temporal tasks, but their behavior is stron…
Understanding Transformers for Time Series: Rank Structure, Flow-of-ranks, and Compressibility
Annan Yu, Danielle C. Maddix, Boran Han +7
Transformers are widely used across data modalities, and yet the principles distilled from text models often transfer imperfectly to models trained to other modalities. In this pap…
Efficiently Generating Correlated Sample Paths from Multi-step Time Series Foundation Models
Ethan Baron, Boris Oreshkin, Ruijun Ma +5
Many time series applications require access to multi-step forecast trajectories in the form of sample paths. Recently, time series foundation models have leveraged multi-step look…
When Does Multimodality Lead to Better Time Series Forecasting?
Xiyuan Zhang, Boran Han, Haoyang Fang +11
Recently, there has been growing interest in incorporating textual information into foundation models for time series forecasting. However, it remains unclear whether and under wha…
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…
LLMForecaster: Improving Seasonal Event Forecasts with Unstructured Textual Data
Hanyu Zhang, Chuck Arvin, Dmitry Efimov +5
Modern time-series forecasting models often fail to make full use of rich unstructured information about the time series themselves. This lack of proper conditioning can lead to ob…
Transferring Knowledge from Large Foundation Models to Small Downstream Models
Shikai Qiu, Boran Han, Danielle C. Maddix +3
How do we transfer the relevant knowledge from ever larger foundation models into small, task-specific downstream models that can run at much lower costs? Standard transfer learnin…
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…