金融时间序列(预测分析 / 相似度 / 数据处理)
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Updated
Jul 10, 2024 - Jupyter Notebook
金融时间序列(预测分析 / 相似度 / 数据处理)
This repository implements an XGBoost model for predicting the prices of financial instruments, such as stocks and cryptocurrencies. Using gradient boosting techniques, it aims to capture patterns in price movements, enhancing prediction accuracy across various datasets.
Self-healing MLOps pipeline for bankruptcy prediction: Airflow DAGs, MLflow registry, Evidently drift-triggered retraining, Terraform on AWS.
Our startup, Mela, aims to simplify cryptocurrency trading for everyone and provide reliable investment sources while mitigating risks. We aim to design and build a reliable, large-scale trading data pipeline that can run various backtests and store useful artifacts in a robust data warehouse.
Predicting financial time series with Long Short-Term Memory (LSTM) networks.
AI-powered market prediction platform with complete MLOps pipeline, REST API, Android app, Streamlit dashboard, DVC versioning, and real-time financial insights.
Financial Market Prediction using Regression on Sequential Time-Series | ML Mini Project | Python, Scikit-learn, Pandas | BTech CSE @ BIHER
Predictive analysis of Hermes and BlackRock financial returns using AR and ARMA models for dynamic portfolio management.
End-to-end market capitalization forecasting system using TimeSeriesTransformer, ARIMA and RNN models with automated data pipeline from Yahoo Finance
Publications and research themes with explicit contribution and disclosure boundaries.
This repository implements a WaveNet model for predicting financial instrument prices, such as currencies, stocks, and cryptocurrencies, using advanced AI techniques like gradient boosting to capture intricate patterns in price movements.
This repository implements the KNeighbors Regressor (KNN) model for predicting financial instrument prices such as stocks, currencies, and cryptocurrencies. It leverages gradient boosting techniques to improve accuracy by capturing complex patterns in price movements.
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