Tag
Nexus introduces a multi-agent framework that decomposes time series forecasting into specialized stages, integrating numerical patterns and contextual information using LLMs, achieving state-of-the-art results on benchmarks.
This academic paper challenges the effectiveness of long-context scaling in time series forecasting, demonstrating that retrieval-based methods outperform standard architectures like PatchTST and foundation models such as Chronos and Moirai.
This paper introduces Gated QKAN-FWP, a scalable quantum-inspired sequence learning framework that combines Fast Weight Programmers with Kolmogorov-Arnold Networks using single-qubit data re-uploading circuits.
This paper presents an end-to-end pipeline for identifying and forecasting green skill demand using online job postings from Mexico's automotive industry. It benchmarks 15 time-series forecasting models, finding transformer-based models like FEDformer and Informer perform best, and introduces a two-dimensional framework to classify skills by growth dynamics.
Kronos is a new foundation model for financial K-line data that uses a specialized tokenizer and autoregressive pre-training to outperform existing models in forecasting and synthetic data generation.