manateechen

AIAlpha

Use unsupervised and supervised learning to predict stocks

air-pollution

django application - forecasting koare air pollution

CMAQ_Installation_tutorial

CMAQ 5.3.1 installation guide based on intel compilers.

Deep-Learning--Forecasting-Air-Pollution-in-Beijing-China-Machine-Learning-

deep-learning-time-series

List of papers, code and experiments using deep learning for time series forecasting

DeepLearningForTSF

深度学习以进行时间序列预测

DeepTrade

A LSTM model using Risk Estimation loss function for stock trades in market

leaflet-velocity

Visualise velocity data on a leaflet layer

load_forecasting

Load forcasting on Delhi area electric power load using ARIMA, RNN, LSTM and GRU models

LSTM-Neural-Network-for-Time-Series-Prediction

LSTM built using Keras Python package to predict time series steps and sequences. Includes sin wave and stock market data

MachineLearningStocks

Using python and scikit-learn to make stock predictions

MarketDataScience

meic2wrf

Interpolating & distributing MEIC 0.25*0.25 emission inventory onto WRF-Chem grids

neural-doodle

Turn your two-bit doodles into fine artworks with deep neural networks, generate seamless textures from photos, transfer style from one image to another, perform example-based upscaling, but wait... there's more! (An implementation of Semantic Style Transfer.)

OBS_CSV_TO_LITTLE_R

Take daily meteorological data files in csv format (combine them w/ some data processing) and write data to little_r format for WRF OBSGRID (and WRFDA? - untested)

Paper-Implementation-DSTP-RNN-For-Stock-Prediction-Based-On-DA-RNN

基於DA-RNN之DSTP-RNN論文試做(Ver1.0)

PatchTST

An offical implementation of PatchTST: "A Time Series is Worth 64 Words: Long-term Forecasting with Transformers." (ICLR 2023) https://arxiv.org/abs/2211.14730

PyChEmiss

Create WRF-Chem emission file from your local emissions disaggregated in space and time.

seq2tens

Seq2Tens: An efficient representation of sequences by low-rank tensor projections

sjtrade

shioaji day trading demo package

Solve-Air-Air-Pollution-Forecasting-using-Deep-Attentive-Sequence-Learning

First step towards solving a real-life problem - air pollution forecasting in Delhi, using deep learning

spacetime

Code for SpaceTime 🌌⏱️. Proposed in Effectively Modeling Time Series with Simple Discrete State Spaces, ICLR 2023.

Stock-market-forecasting

Forecasting directional movements of stock prices for intraday trading using LSTM and random forest

surpriver

Find big moving stocks before they move using machine learning and anomaly detection

TECA

TECA, theToolkit for Extreme Climate Analysis, contains a collection of climate anlysis algorithms targetted at extreme event detection and analysis.

Time-Series-Forecasting-of-Amazon-Stock-Prices-using-Neural-Networks-LSTM-and-GAN-

Project analyzes Amazon Stock data using Python. Feature Extraction is performed and ARIMA and Fourier series models are made. LSTM is used with multiple features to predict stock prices and then sentimental analysis is performed using news and reddit sentiments. GANs are used to predict stock data too where Amazon data is taken from an API as Generator and CNNs are used as discriminator.

transformer-time-series-prediction

proof of concept for a transformer-based time series prediction model

ts2vec

A universal time series representation learning framework

umwm

University of Miami Wave Model

WPS-ghrsst-to-intermediate

Converts JPL PODAAC GHRSST netCDF files to WPS Intermediate version 5 files