Gathers machine learning and deep learning models for Stock forecasting including trading bots and simulations

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

Jupyter Notebook tutorials on solving real-world problems with Machine Learning & Deep Learning using PyTorch. Topics: Face detection with Detectron 2, Time Series anomaly detection with LSTM Autoencoders, Object Detection with YOLO v5, Build your first Neural Network, Time Series forecasting for Coronavirus daily cases, Sentiment Analysis with BER

Deep learning PyTorch library for time series forecasting, classification, and anomaly detection (originally for flood forecasting).

A Python toolkit/library for reality-centric machine/deep learning and data mining on partially-observed time series, including SOTA neural network models for scientific analysis tasks of imputation/classification/clustering/forecasting/anomaly detection/cleaning on incomplete industrial (irregularly-sampled) multivariate TS with NaN missing values

Resources about time series forecasting and deep learning.

A tutorial demonstrating how to implement deep learning models for time series forecasting

Awesome Easy-to-Use Deep Time Series Modeling based on PaddlePaddle, including comprehensive functionality modules like TSDataset, Analysis, Transform, Models, AutoTS, and Ensemble, etc., supporting versatile tasks like time series forecasting, representation learning, and anomaly detection, etc., featured with quick tracking of SOTA deep models.

Code for our NeurIPS 2019 paper "Shape and Time Distortion Loss for Training Deep Time Series Forecasting Models"

PyTorch code for Learning Deep Time-index Models for Time Series Forecasting (ICML 2023)

PyTorch GPU implementation of the ES-RNN model for time series forecasting

[ITS'21] Human Trajectory Forecasting in Crowds: A Deep Learning Perspective

Stanford Project: Artificial Intelligence is changing virtually every aspect of our lives. Today’s algorithms accomplish tasks that until recently only expert humans could perform. As it relates to finance, this is an exciting time to adopt a disruptive technology that will transform how everyone invests for generations. Models that explain the returns of individual stocks generally use company and stock characteristics, e.g., the market prices of financial instruments and companies’ accounting data. These characteristics can also be used to predict expected stock returns out-of-sample. Most studies use simple linear models to form these predictions [1] or [2]. An increasing body of academic literature documents that more sophisticated tools from the Machine Learning (ML) and Deep Learning (DL) repertoire, which allow for nonlinear predictor interactions, can improve the stock return forecasts [3], [4] or [5]. The main goal of this project is to investigate whether modern DL techniques can be utilized to more efficiently predict the movements of the stock market. Specifically, we train a LSTM neural network with time series price-volume data and compare its out-of-sample return predictability with the performance of a simple logistic regression (our baseline model).

PyTorch implementation of Transformer model used in "Deep Transformer Models for Time Series Forecasting: The Influenza Prevalence Case"

Code for Deep-learning Architecture for Short-term Passenger Flow Forecasting in Urban Rail Transit

About code release of "Interpretable Weather Forecasting for Worldwide Stations with a Unified Deep Model", Nature Machine Intelligence, 2023. https://www.nature.com/articles/s42256-023-00667-9

基于深度学习的多特征电力负荷预测

Paper:Spatio-Temporal Graph Convolutional Networks: A Deep Learning Framework for Traffic Forecasting . Implementation of spatio-temporal graph convolutional network with PyTorch

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This repository is designed to teach you, step-by-step, how to develop deep learning methods for time series forecasting with concrete and executable examples in Python.

Deep learning for forecasting company fundamental data

An experiemtal review on deep learning architectures for time series forecasting

Deep Adaptive Input Normalization for Time Series Forecasting

Deep learning PyTorch library for time series forecasting

An R package with Python support for multi-step-ahead forecasting with machine learning and deep learning algorithms

Package towards building Explainable Forecasting and Nowcasting Models with State-of-the-art Deep Neural Networks and Dynamic Factor Model on Time Series data sets with single line of code. Also, provides utilify facility for time-series signal similarities matching, and removing noise from timeseries signals.

The code of the paper 'Deep Forecast : Deep Learning-based Spatio-Temporal Forecasting", ICML Time Series Workshop 2017.

This repository provides an open source implementation of the Spatio-Temporal GAT introduced by Zhang et al in "Spatial-Temporal Graph Attention Networks:A Deep Learning Approach for Traffic Forecasting" https://ieeexplore.ieee.org/stamp/stamp.jsp?tp=&arnumber=8903252

short-term load forecasting with deep residual networks

Code associated with the paper 'Seasonal Arctic sea ice forecasting with probabilistic deep learning'