The source code and data of the paper "Instance-wise Graph-based Framework for Multivariate Time Series Forecasting".

Wentao Xu

Last update: Dec 5, 2022

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Deep Learning IGMTF

Overview

IGMTF

The source code and data of the paper "Instance-wise Graph-based Framework for Multivariate Time Series Forecasting".

Requirements

The framework is implemented using python3 with dependencies specified in requirements.txt

Running the code

git clone https://github.com/Wentao-Xu/IGMTF.git
cd IGMTF
tar -zxvf data.tar.gz
mkdir model

Traffic

# Horizon 3
python learn.py --save ./model/model-traffic-3.pt --data ./data/traffic.txt --num_nodes 862 --epoch 100 --horizon 3 --hidden_size 256 --hidden_batch_size 64 --k_day 30 --n_neighbor 20

# Horizon 6
python learn.py --save ./model/model-traffic-6.pt --data ./data/traffic.txt --num_nodes 862 --epoch 100 --horizon 6 --hidden_size 256 --hidden_batch_size 64 --k_day 5 --n_neighbor 30

# Horizon 12
python learn.py --save ./model/model-traffic-12.pt --data ./data/traffic.txt --num_nodes 862 --epoch 100 --horizon 12 --hidden_size 256 --hidden_batch_size 64 --k_day 10 --n_neighbor 30

# Horizon 24
python learn.py --save ./model/model-traffic-24.pt --data ./data/traffic.txt --num_nodes 862 --epoch 100 --horizon 24 --hidden_size 256 --hidden_batch_size 64 --k_day 3 --n_neighbor 10

Electricity

# Horizon 3
python learn.py --save ./model/model-electricity-3.pt --data ./data/electricity.txt --num_nodes 321 --epoch 100 --horizon 3 --hidden_size 512 --k_day 5 --n_neighbor 20

# Horizon 6
python learn.py --save ./model/model-electricity-6.pt --data ./data/electricity.txt --num_nodes 321 --epoch 100 --horizon 6 --hidden_size 512 --k_day 3 --n_neighbor 3

# Horizon 12
python learn.py --save ./model/model-electricity-12.pt --data ./data/electricity.txt --num_nodes 321 --epoch 100 --horizon 12 --hidden_size 512 --k_day 10 --n_neighbor 5

# Horizon 24
python learn.py --save ./model/model-electricity-24.pt --data ./data/electricity.txt --num_nodes 321 --epoch 100 --horizon 24 --hidden_size 512 --k_day 5 --n_neighbor 20

Exchange-Rate

# Horizon 3
python learn.py --save ./model/model-exchange-3.pt --data ./data/exchange_rate.txt --num_nodes 8 --epoch 100 --horizon 3 --hidden_size 512 --batch_size 16 --k_day 20 --n_neighbor 20

# Horizon 6
python learn.py --save ./model/model-exchange-6.pt --data ./data/exchange_rate.txt --num_nodes 8 --epoch 100 --horizon 6 --hidden_size 512 --batch_size 16 --k_day 5 --n_neighbor 10

# Horizon 12
python learn.py --save ./model/model-exchange-12.pt --data ./data/exchange_rate.txt --num_nodes 8 --epoch 100 --horizon 12 --hidden_size 512 --batch_size 16 --k_day 10 --n_neighbor 10

# Horizon 24
python learn.py --save ./model/model-exchange-24.pt --data ./data/exchange_rate.txt --num_nodes 8 --epoch 100 --horizon 24 --hidden_size 512 --batch_size 16 --k_day 5 --n_neighbor 20

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The source code and data of the paper "Instance-wise Graph-based Framework for Multivariate Time Series Forecasting".

Related tags

Overview

IGMTF

Requirements

Running the code

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The source code and data of the paper "Instance-wise Graph-based Framework for Multivariate Time Series Forecasting".

Related tags

Overview

IGMTF

Requirements

Running the code

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Inference code for "StylePeople: A Generative Model of Fullbody Human Avatars" paper. This code is for the part of the paper describing video-based avatars.

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Code for the prototype tool in our paper "CoProtector: Protect Open-Source Code against Unauthorized Training Usage with Data Poisoning".

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