Chronicle is the first model jointly pretrained from scratch on text and time series in a unified transformer that matches a comparable language model on NLU tasks and sets new bars for time series classification and multimodal forecasting.
Journal of Machine Learning Research , volume =
2 Pith papers cite this work. Polarity classification is still indexing.
2
Pith papers citing it
years
2026 2verdicts
UNVERDICTED 2representative citing papers
DWT decomposes sentence- or word-level embeddings into multi-resolution components that preserve semantics for direct or LLM-guided summarization, yielding up to 97% fidelity and gains in BERTScore and semantic metrics over GPT-4o baselines on clinical and legal benchmarks.
citing papers explorer
-
Chronicle: A Multimodal Foundation Model for Joint Language and Time Series Understanding
Chronicle is the first model jointly pretrained from scratch on text and time series in a unified transformer that matches a comparable language model on NLU tasks and sets new bars for time series classification and multimodal forecasting.
-
DWTSumm: Discrete Wavelet Transform for Document Summarization
DWT decomposes sentence- or word-level embeddings into multi-resolution components that preserve semantics for direct or LLM-guided summarization, yielding up to 97% fidelity and gains in BERTScore and semantic metrics over GPT-4o baselines on clinical and legal benchmarks.