OKG-LLM constructs an Ocean Knowledge Graph, learns its embeddings, fuses them with SST observations, and applies an LLM to outperform prior methods on global sea surface temperature prediction.
Prompt-based time series forecasting: A new task and dataset
6 Pith papers cite this work. Polarity classification is still indexing.
6
Pith papers citing it
citation-role summary
background 1
citation-polarity summary
fields
cs.LG 6roles
background 1polarities
background 1representative citing papers
This survey and benchmark of deep time series models using the released TSLib library finds that models with specific structures perform well only on distinct analysis tasks.
Chronos pretrains transformer models on tokenized time series to deliver strong zero-shot forecasting across diverse domains.
Time-LLM reprograms frozen LLMs for time series forecasting via text prototypes and Prompt-as-Prefix, outperforming specialized models in standard, few-shot, and zero-shot settings.
TiRex-2 is a recurrent xLSTM time series foundation model for multivariate forecasting with future covariates and constant-cost streaming that reports SOTA zero-shot results on GIFT-Eval and fev-bench.
GeoGNN is a two-tower GNN that learns geographic cell embeddings from adjacency graphs and matches them to temporal representations via dot-product similarity plus classification, improving geolocalization accuracy by ~27% on electricity datasets.
citing papers explorer
-
Deep Time Series Models: A Comprehensive Survey and Benchmark
This survey and benchmark of deep time series models using the released TSLib library finds that models with specific structures perform well only on distinct analysis tasks.
-
Chronos: Learning the Language of Time Series
Chronos pretrains transformer models on tokenized time series to deliver strong zero-shot forecasting across diverse domains.