1579 Open source projects that are alternatives of or similar to Pytorch Gan Timeseries

A collection of anomaly detection methods (iid/point-based, graph and time series) including active learning for anomaly detection/discovery, bayesian rule-mining, description for diversity/explanation/interpretability. Analysis of incorporating label feedback with ensemble and tree-based detectors. Includes adversarial attacks with Graph Convolutional Network.

MTSS-GAN: Multivariate Time Series Simulation with Generative Adversarial Networks (by @firmai)

Example projects I completed to understand Deep Learning techniques with Tensorflow. Please note that I do no longer maintain this repository.

Using LSTM network for time series forecasting

This library was developed for economic analysis in the Brazilian scenario (Investments, micro and macroeconomic indicators)

Annotation of ECG signals using deep learning, tensorflow’ Keras

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

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Synthetic Data Generation for mixed-type, multivariate time series.

DeltaPy - Tabular Data Augmentation (by @firmai)

Tensorflow implementation of paper: Learning to Diagnose with LSTM Recurrent Neural Networks.

Machine learning, in numpy

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

Synthetic Data Generation for tabular, relational and time series data.

Quantitative systematic trading strategy development and backtesting in Julia

ConvLSTM/ConvGRU (Encoder-Decoder) with PyTorch on Moving-MNIST

5th place solution for Kaggle competition Favorita Grocery Sales Forecasting

Anomaly detection for temporal data using LSTMs

Rainfall analysis of Maharashtra - Season/Month wise forecasting. Different methods have been used. The main goal of this project is to increase the performance of forecasted results during rainy seasons.

The purpose of this repository is to make prototypes as case study in the context of proof of concept(PoC) and research and development(R&D) that I have written in my website. The main research topics are Auto-Encoders in relation to the representation learning, the statistical machine learning for energy-based models, adversarial generation networks(GANs), Deep Reinforcement Learning such as Deep Q-Networks, semi-supervised learning, and neural network language model for natural language processing.

Remaining Useful Life (RUL) estimation of Lithium-ion batteries using deep LSTMs

A stock trading bot that uses machine learning to make price predictions.

Sequence prediction using recurrent neural networks(LSTM) with TensorFlow

A collection of time series prediction methods: rnn, seq2seq, cnn, wavenet, transformer, unet, n-beats, gan, kalman-filter

[IMC 2020 (Best Paper Finalist)] Using GANs for Sharing Networked Time Series Data: Challenges, Initial Promise, and Open Questions

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 BERT.

MSGARCH R Package

PyTorch/Tensorflow solutions for Stanford's CS231n: "CNNs for Visual Recognition"

AtsPy: Automated Time Series Models in Python (by @firmai)

Quantitative Financial Modelling Framework

Deep Convolutional Bidirectional LSTM for Complex Activity Recognition with Missing Data. Human Activity Recognition Challenge. Springer SIST (2020)

ARCH models in Python

Notebooks for financial economics. Keywords: Jupyter notebook pandas Federal Reserve FRED Ferbus GDP CPI PCE inflation unemployment wage income debt Case-Shiller housing asset portfolio equities SPX bonds TIPS rates currency FX euro EUR USD JPY yen XAU gold Brent WTI oil Holt-Winters time-series forecasting statistics econometrics

A Keras library for multi-step time-series forecasting.

Library of autoencoders for sequential data

Financial market technical analysis & indicators in Julia

Time series prediction using dilated causal convolutional neural nets (temporal CNN)

Seq2Seq, Bert, Transformer, WaveNet for time series prediction.

This repository is a non-official implementation of TimeGAN (Yoon et al., NIPS2019) using PyTorch.

Using Bidirectional Generative Adversarial Networks to estimate Value-at-Risk for Market Risk Management using TensorFlow.

Tools for financial economics. Curated wrapper over Python ecosystem. Source code for fecon235 Jupyter notebooks.

Investment portfolio and stocks analyzing tools for Python with free historical data

A framework for using LSTMs to detect anomalies in multivariate time series data. Includes spacecraft anomaly data and experiments from the Mars Science Laboratory and SMAP missions.

Implementation of RNN for Time Series prediction from the paper https://arxiv.org/abs/1704.02971

CNN for stock market prediction using raw data & candlestick graph.

RNN and general weights, gradients, & activations visualization in Keras & TensorFlow

Projecting images to latent space with StyleGAN2.

Examples demonstrating the use of oandapyV20 (oanda-api-v20)

Stock Market Prediction Web App based on Machine Learning and Sentiment Analysis of Tweets (API keys included in code). The front end of the Web App is based on Flask and Wordpress. The App forecasts stock prices of the next seven days for any given stock under NASDAQ or NSE as input by the user. Predictions are made using three algorithms: ARIMA, LSTM, Linear Regression. The Web App combines the predicted prices of the next seven days with the sentiment analysis of tweets to give recommendation whether the price is going to rise or fall

A Pytorch implementation of Stylegan2 with UNet Discriminator

Module for portfolio optimization, prices and options

I used in this project a reccurent neural network to generate c code based on a dataset of c files from the linux repository.

利用python进行财务分析

PyTorch Language Model for 1-Billion Word (LM1B / GBW) Dataset

The deeplearning algorithms implemented by tensorflow

Stock market tracker for hackers.

Carbon is one of the components of Graphite, and is responsible for receiving metrics over the network and writing them down to disk using a storage backend.

Spectral Normalization for Keras Dense and Convolution Layers

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

GridDB is a next-generation open source database that makes time series IoT and big data fast,and easy.