# FinMultiTime: A Four-Modal Bilingual Dataset for Financial Time-Series Analysis

arXiv: https://arxiv.org/abs/2506.05019 · HTML: https://mlsanity.com/paper/2506.05019/

## Key facts

- Cited by 11 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 5 Jun 2025.

## Built on by (most cited first, 11 of 11)

- [SIFThinker: Spatially-Aware Image Focus for Visual Reasoning](https://mlsanity.com/paper/2508.06259/) (2025)
- [SciTS: Scientific Time Series Understanding and Generation with LLMs](https://mlsanity.com/paper/2510.03255/) (2025)
- [Overcoming the Modality Gap in Context-Aided Forecasting](https://mlsanity.com/paper/2603.12451/) (2026)
- [LLaTiSA: Towards Difficulty-Stratified Time Series Reasoning from Visual Perception to Semantics](https://mlsanity.com/paper/2604.17295/) (2026)
- [Coarse-to-Fine Personalized LLM Impressions for Streamlined Radiology Reports](https://mlsanity.com/paper/2508.15845/) (2025)
- [Do VLMs Truly "Read" Candlesticks? A Multi-Scale Benchmark for Visual Stock Price Forecasting](https://mlsanity.com/paper/2604.12659/) (2026)
- [RAVEN: A Regime-Aware Variable-context Expert Network for Financial Time Series Forecasting](https://mlsanity.com/paper/2606.24062/) (2026)
- [TimeBraid: Unifying Time Series and Language for Understanding and Forecasting](https://mlsanity.com/paper/2609.29792/) (2026)
- [DualCast: A Dual-Path Language Model for Bimodal Financial Time-Series Forecasting](https://mlsanity.com/paper/2609.38197/) (2026)
- [GIFT-Eval-CTX: A Benchmark For Contextual Forecasting Model Evaluation](https://mlsanity.com/paper/2610.17794/) (2026)
- [MMTA: Benchmarking Multimodal Temporal Analysis with Time Series, Text, and Vision](https://mlsanity.com/paper/2610.17856/) (2026)

## Abstract (arXiv)

Pure time series forecasting tasks typically focus exclusively on numerical features; however, real-world financial decision-making demands the comparison and analysis of heterogeneous sources of information. Recent advances in deep learning and large scale language models (LLMs) have made significant strides in capturing sentiment and other qualitative signals, thereby enhancing the accuracy of financial time series predictions. Despite these advances, most existing datasets consist solely of price series and news text, are confined to a single market, and remain limited in scale. In this paper, we introduce FinMultiTime, the first large scale, multimodal financial time series dataset. FinMultiTime temporally aligns four distinct modalities financial news, structured financial tables, K-line technical charts, and stock price time series across both the S&P 500 and HS 300 universes. Covering 5,105 stocks from 2009 to 2025 in the United States and China, the dataset totals 112.6 GB and provides minute-level, daily, and quarterly resolutions, thus capturing short, medium, and long term market signals with high fidelity. Our experiments demonstrate that (1) scale and data quality markedly boost prediction accuracy; (2) multimodal fusion yields moderate gains in Transformer models; and (3) a fully reproducible pipeline enables seamless dataset updates.