XiaWei's starred repositories
Stock-trends-prediction-with-macroeconomic-indicators
Stock markets are an essential component of the economy. Their prediction naturally arouses afascination in the academic and financial world. Indeed, financial time series, due to their widerange application fields, have seen numerous studies being published for their prediction. Some ofthese studies aim to establish whether there is a strong and predictive link between macroeconomicindicators and stock market trends and thus predict market returns. Stock market prediction howeverremains a challenging task due to uncertain noise. To what extent can macroeconomic indicatorsbe strong predictors of stock price ? Can they be used for stock trends modeling ? To answer thesequestions, we will focus on several time series forecasting models. We will on the one hand usestatistical time series models, more specifically the most commonly used time series approachesfor stock prediction : the Autoregressive Integrated Moving Average (ARIMA), the GeneralizedAutoregressive Conditional Heteroscedasticity (GARCH) and the Vector Autoregressive (VAR)approach. On the other hand, we will be using two deep learning models : the Long-Short TermMemory (LSTM) and the Gated Recurrent Unit (GRU) for our prediction task. In the final section ofthis paper, we look directly at companies to detect trends
GPU-Parallel-Genetic-Algorithm-using-CUDA-with-Python-Numba
Implementation of a GPU-parallel Genetic Algorithm using CUDA with python numba for significant speedup.
pytorch-ts
PyTorch based Probabilistic Time Series forecasting framework based on GluonTS backend
pytorch-forecasting
Time series forecasting with PyTorch
2018-CCF-BDCI--TOP3
参赛者需要根据给出的基金净值、基金业绩比较基准、对应指数行情、基金间相关性等数据，构建模型、算法进行训练。
BDCI-2018-Supply-Chain-Demand-Forecast
初赛Rank1 复赛Rank1 2018 CCF 大数据与计算智能大赛 供应链需求预测 Miracccccccle
supplychainpy
Supplychainpy is a Python library for supply chain analysis, modelling and simulation. The library assists a workflow that is reliant on Excel and VBA.
Travelling_Salesman_Optimization
Python implementation for TSP using Genetic Algorithms, Simulated Annealing, PSO (Particle Swarm Optimization), Dynamic Programming, Brute Force, Greedy and Divide and Conquer
Time-Series-ARIMA-XGBOOST-RNN
Time series forecasting for individual household power prediction: ARIMA, xgboost, RNN
iTransformer
Official implementation for "iTransformer: Inverted Transformers Are Effective for Time Series Forecasting" (ICLR 2024 Spotlight), https://openreview.net/forum?id=JePfAI8fah
Python-GSGP
My implementarion of the Geometric Semantic Genetic Programming (GSGP) algorithm.
flow-forecast
Deep learning PyTorch library for time series forecasting, classification, and anomaly detection (originally for flood forecasting).
Time-series-prediction
tfts: Time Series Deep Learning Models in TensorFlow
transformer-time-series-prediction
proof of concept for a transformer-based time series prediction model
Deep-Time-Series-Prediction
Seq2Seq, Bert, Transformer, WaveNet for time series prediction.
AISTransforemr
利用transformer进行船舶轨迹预测。
Physics-informed-Transformer-IDM
The implementation of the paper "A Physics-Informed Transformer Model for Vehicle Trajectory Prediction on Highways". The paper is now under review.
Artificial-Potential-Field
机器人导航--人工势场法及其改进
aritificial-potential-field
Artificial Potential Field for path planning
TMR-4930-Ship-trajectory-prediction-in-confined-waters
TMR 4930 Ship trajectory prediction in confined waters