Introduces the task of counterfactual time series forecasting with textual conditions plus a text-attribution mechanism that improves accuracy by distinguishing mutable from immutable factors.
arXiv preprint arXiv:2406.01638 , year=
3 Pith papers cite this work. Polarity classification is still indexing.
3
Pith papers citing it
citation-role summary
background 1
citation-polarity summary
roles
background 1polarities
background 1representative citing papers
MAP4TS combines global, local, statistical, and temporal prompts derived from classical time-series analysis with raw embeddings via cross-modality alignment to improve LLM forecasting performance across eight datasets.
A survey proposing a taxonomy of Injective, Bridging, and Internal Alignment paradigms to evolve TSA into user-driven Time Series Question Answering with LLMs.
citing papers explorer
-
What if Tomorrow is the World Cup Final? Counterfactual Time Series Forecasting with Textual Conditions
Introduces the task of counterfactual time series forecasting with textual conditions plus a text-attribution mechanism that improves accuracy by distinguishing mutable from immutable factors.
-
MAP4TS: A Multi-Aspect Prompting Framework for Time-Series Forecasting with Large Language Models
MAP4TS combines global, local, statistical, and temporal prompts derived from classical time-series analysis with raw embeddings via cross-modality alignment to improve LLM forecasting performance across eight datasets.
-
From Time Series Analysis to Question Answering: A Survey in the LLM Era
A survey proposing a taxonomy of Injective, Bridging, and Internal Alignment paradigms to evolve TSA into user-driven Time Series Question Answering with LLMs.