Finding 4269Emerging EvidenceValidation V0
Larger, high-quality datasets improve forecasting accuracy, with Transformer models outperforming RNN, LSTM, GRU, CNN and TimesNet to achieve $R^2\approx0.97$ on 35-stock sets, while smaller data treat extra modalities as noise.
78%Confidence
1Evidence objects
v1Version
DraftStatus
Evidence trail
Supporting78% linkage confidence
Larger, high-quality datasets improve forecasting accuracy, with Transformer models outperforming RNN, LSTM, GRU, CNN and TimesNet to achieve $R^2\approx0.97$ on 35-stock sets, while smaller data treat extra modalities as noise.
key_findings bullet 2 · key_findings
Inspect source: FinMultiTime: A Four-Modal Bilingual Dataset for Financial Time-Series Analysis →This Finding was extracted from the configured corpus. It is versioned, traceable, and may evolve through editorial review or new corpus evidence.