Deep learning-based approach to discovering Granger causality networks in multivariate time series

Overview

Neural Granger Causality

The Neural-GC repository contains code for a deep learning-based approach to discovering Granger causality networks in multivariate time series. The methods implemented here are described in this paper.

Installation

To install the code, please clone the repository. All you need is Python 3, PyTorch (>= 0.4.0), numpy and scipy.

Usage

See examples of how to apply our approach in the notebooks cmlp_lagged_var_demo.ipynb, clstm_lorenz_demo.ipynb, and crnn_lorenz_demo.ipynb.

How it works

The models implemented in this repository, called the cMLP, cLSTM and cRNN, are neural networks that model multivariate time series by forecasting each time series separately. During training, sparse penalties on the input layer's weight matrix set groups of parameters to zero, which can be interpreted as discovering Granger non-causality.

The cMLP model can be trained with three different penalties: group lasso, group sparse group lasso, and hierarchical. The cLSTM and cRNN models both use a group lasso penalty, and they differ from one another only in the type of RNN cell they use.

Training models with non-convex loss functions and non-smooth penalties requires a specialized optimization strategy, and we use a proximal gradient descent approach (ISTA). Our paper finds that ISTA provides comparable performance to two other approaches: proximal gradient descent with a line search (GISTA), which guarantees convergence to a local minimum, and Adam, which converges faster (although it requires an additional thresholding parameter).

Other information

  • Selecting the right regularization strength can be difficult and time consuming. To get results for many regularization strengths, you may want to run parallel training jobs or use a warm start strategy.
  • Pretraining (training without regularization) followed by ISTA can lead to a different result than training directly with ISTA. Given the non-convex objective function, this is unsurprising, because the initialization from pretraining is very different than a random initialization. You may need to experiment to find what works best for you.
  • If you want to train a debiased model with the learned sparsity pattern, use the cMLPSparse, cLSTMSparse, and cRNNSparse classes.

Authors

References

  • Alex Tank, Ian Covert, Nicholas Foti, Ali Shojaie, Emily Fox. "Neural Granger Causality." Transactions on Pattern Analysis and Machine Intelligence, 2021.
Owner
Ian Covert
PhD student at University of Washington.
Ian Covert
An elaborate and exhaustive paper list for Named Entity Recognition (NER)

Named-Entity-Recognition-NER-Papers by Pengfei Liu, Jinlan Fu and other contributors. An elaborate and exhaustive paper list for Named Entity Recognit

Pengfei Liu 388 Dec 18, 2022
An AutoML Library made with Optuna and PyTorch Lightning

An AutoML Library made with Optuna and PyTorch Lightning Installation Recommended pip install -U gradsflow From source pip install git+https://github.

GradsFlow 294 Dec 17, 2022
🔪 Elimination based Lightweight Neural Net with Pretrained Weights

ELimNet ELimNet: Eliminating Layers in a Neural Network Pretrained with Large Dataset for Downstream Task Removed top layers from pretrained Efficient

snoop2head 4 Jul 12, 2022
Lightweight, Portable, Flexible Distributed/Mobile Deep Learning with Dynamic, Mutation-aware Dataflow Dep Scheduler; for Python, R, Julia, Scala, Go, Javascript and more

Apache MXNet (incubating) for Deep Learning Apache MXNet is a deep learning framework designed for both efficiency and flexibility. It allows you to m

The Apache Software Foundation 20.2k Jan 08, 2023
A clear, concise, simple yet powerful and efficient API for deep learning.

The Gluon API Specification The Gluon API specification is an effort to improve speed, flexibility, and accessibility of deep learning technology for

Gluon API 2.3k Dec 17, 2022
LoL Runes Recommender With Python

LoL-Runes-Recommender Para ejecutar la aplicación se debe llamar a execute_app.p

Sebastián Salinas 1 Jan 10, 2022
Convolutional Neural Networks

Darknet Darknet is an open source neural network framework written in C and CUDA. It is fast, easy to install, and supports CPU and GPU computation. D

Joseph Redmon 23.7k Jan 05, 2023
Controlling Hill Climb Racing with Hand Tacking

Controlling Hill Climb Racing with Hand Tacking Opened Palm for Gas Closed Palm for Brake

Rohit Ingole 3 Jan 18, 2022
SPT_LSA_ViT - Implementation for Visual Transformer for Small-size Datasets

Vision Transformer for Small-Size Datasets Seung Hoon Lee and Seunghyun Lee and Byung Cheol Song | Paper Inha University Abstract Recently, the Vision

Lee SeungHoon 87 Jan 01, 2023
On Uncertainty, Tempering, and Data Augmentation in Bayesian Classification

Understanding Bayesian Classification This repository hosts the code to reproduce the results presented in the paper On Uncertainty, Tempering, and Da

Sanyam Kapoor 18 Nov 17, 2022
Pytorch-Swin-Unet-V2 - a modified version of Swin Unet based on Swin Transfomer V2

Swin Unet V2 Swin Unet V2 is a modified version of Swin Unet arxiv based on Swin

Chenxu Peng 26 Dec 03, 2022
[ICCV 2021] Group-aware Contrastive Regression for Action Quality Assessment

CoRe Created by Xumin Yu*, Yongming Rao*, Wenliang Zhao, Jiwen Lu, Jie Zhou This is the PyTorch implementation for ICCV paper Group-aware Contrastive

Xumin Yu 31 Dec 24, 2022
SMPL-X: A new joint 3D model of the human body, face and hands together

SMPL-X: A new joint 3D model of the human body, face and hands together [Paper Page] [Paper] [Supp. Mat.] Table of Contents License Description News I

Vassilis Choutas 1k Jan 09, 2023
This repo implements a 3D segmentation task for an airport baggage dataset.

3D CT Scan Segmentation With Occupancy Network This repo implements a 3D superresolution segmentation task for an airport baggage dataset. Our final p

Christoph Reich 2 Mar 28, 2022
Simple ray intersection library similar to coldet - succedeed by libacc

Ray Intersection This project offers a header only acceleration structure library including implementations for a BVH- and KD-Tree. Applications may i

Nils Moehrle 29 Jun 23, 2022
Unofficial PyTorch implementation of Guided Dropout

Unofficial PyTorch implementation of Guided Dropout This is a simple implementation of Guided Dropout for research. We try to reproduce the algorithm

2 Jan 07, 2022
3rd Place Solution for ICCV 2021 Workshop SSLAD Track 3A - Continual Learning Classification Challenge

Online Continual Learning via Multiple Deep Metric Learning and Uncertainty-guided Episodic Memory Replay 3rd Place Solution for ICCV 2021 Workshop SS

Rifki Kurniawan 6 Nov 10, 2022
Affine / perspective transformation in Pose Estimation with Tensorflow 2

Pose Transformation Affine / Perspective transformation in Pose Estimation with Tensorflow 2 Introduction 이 repo는 pose estimation을 연구하고 개발하는 데 도움이 되기

Kim Junho 1 Dec 22, 2021
Implementation of the paper NAST: Non-Autoregressive Spatial-Temporal Transformer for Time Series Forecasting.

Non-AR Spatial-Temporal Transformer Introduction Implementation of the paper NAST: Non-Autoregressive Spatial-Temporal Transformer for Time Series For

Chen Kai 66 Nov 28, 2022
Semantic Image Synthesis with SPADE

Semantic Image Synthesis with SPADE New implementation available at imaginaire repository We have a reimplementation of the SPADE method that is more

NVIDIA Research Projects 7.3k Jan 07, 2023