논문 ID: reports/7072880
Long Term Stock Prediction based on Financial Statements
원문 초록
This paper proposes a model with LSTM and fully connected layers to predict long term stock trendings based on financial statements. Two data augmentation techniques are applied on structured data: 1) adding random noise to data fields; 2) erasing partial information from training examples. The performance of the proposed approach is demonstrated on real-world data of about 7,000 stocks listed on Nasdaq.
함께 살펴볼 논문
공통 태그를 근거로 연결했습니다. 실제 인용 관계나 논문의 우수성을 뜻하지 않습니다.
AMBER: Adaptive Mesh Generation by Iterative Mesh Resolution Prediction
Speech Quality Assessment Model Based on Mixture of Experts: System-Level Performance Enhancement and Utterance-Level Challenge Analysis
MultiViT2: A Data-augmented Multimodal Neuroimaging Prediction Framework via Latent Diffusion Model
Individualised Counterfactual Examples Using Conformal Prediction Intervals
SenseShift6D: Multimodal RGB-D Benchmarking for Robust 6D Pose Estimation across Environment and Sensor Variations
AI-Enhanced Pediatric Pneumonia Detection: A CNN-Based Approach Using Data Augmentation and Generative Adversarial Networks (GANs)
토론
이 논문과 연결된 포럼 글이 아직 없습니다.