OliveCUFE's starred repositories
AdaptiveSFM
Adaptive State-Frequency Memory Recurrent Neural Network
My-TensorFlow-tutorials
TensorFlow 学习笔记和分享
transferlearning
Transfer learning / domain adaptation / domain generalization / multi-task learning etc. Papers, codes, datasets, applications, tutorials.-迁移学习
py-faster-rcnn
Faster R-CNN (Python implementation) -- see https://github.com/ShaoqingRen/faster_rcnn for the official MATLAB version
faster_rcnn
Faster R-CNN
equity_volatility_forecasting
Equity Volatility Forecasting using LSTMs
SeatingArrangment_GA
Using genetic algorithm to optimize seating arrangement problem.
python-algoritmo-genetico
Genetic algorithm implementation written in Python. Modeled to find the optimized time for intelligent traffic lights and maximize the traffic flow crossing two streets with random and unbalanced traffic streams.
vrp-genetic-algorithm-python
A simple program written in Python that implements a genetic algorithm for solving the Vehicle Routing Problem (VRP).
TSP-VRP-GENETICS-ALGORITHM
Implementation of TSP and VRP algorithms using a Genetic Algorithm
Genetic-Algorithm-Supply-Chain-
Genetic Algorithm to solve a Multi level Supply Chain Management Problem
group_travel_optimization
Use various optimization techniques, e.g. hill climbing, simulated annealing and genetic algorithm to optimize a group travel planning problem
GeneticAlgorithm
通过遗传算法求解物流配送路径问题
Deep-Learning-Time-Series-Prediction-using-LSTM-Recurrent-Neural-Networks
This project aims to predict VOLATILITY S&P 500 (^VIX) time series using LSTM.
A-Deep-Learning-Based-Illegal-Insider-Trading-Detection-and-Prediction-Technique-in-Stock-Market
Illegal insider trading of stocks is based on releasing non-public information (e.g., new product launch, quarterly financial report, acquisition or merger plan) before the information is made public. Detecting illegal insider trading is difficult due to the complex, nonlinear, and non-stationary nature of the stock market. In this work, we present an approach that detects and predicts illegal insider trading proactively from large heterogeneous sources of structured and unstructured data using a deep-learning based approach combined with discrete signal processing on the time series data. In addition, we use a tree-based approach that visualizes events and actions to aid analysts in their understanding of large amounts of unstructured data. Using existing data, we have discovered that our approach has a good success rate in detecting illegal insider trading patterns. My research paper (IEEE Big Data 2018) on this can be found here: https://arxiv.org/pdf/1807.00939.pdf
EMM-for-stock-prediction
We propose a model to analyze sentiment of online stock forum and use the information to predict stock volatility in the Chinese market. By generating a sentimental dictionary, we analyze the sentimental tendencies of each post as sentiment indicators. Such sentimental information will be fused with market data for prediction based on Recurrent Neural Networks (RNNs). We manually labeled the sentiment of forum post and make the data public available for research. Empirical evidence shows that 8 of the 10 stocks perform better with sentimental indicators.