• Python 3.6
• matplotlib == 3.1.1
• numpy == 1.19.4
• pandas == 0.25.1
• scikit_learn == 0.21.3
• torch == 1.8.0

Dependencies can be installed using the following command:

pip install -r requirements.txt

The dataset for the experiment includes

• ETT(Electricity Eransformer Temperature): The ETT dataset contains two years of data on oil temperature of power transformers in two counties in China and six other metrics.
• KDD(KDD Cup 2018 Dataset): The KDD 2018 Cup dataset, which contains weather data and air pollution data collected by hour from January 30, 2017 to January 30, 2018 for Beijing and London.
• ECL(Electricity Consuming Load): The ECL dataset contains electricity consumption data for 321 clients for the years 2012 to 2014.
• Weather(Weather Dataset): TThe Weather dataset contains weather data collected hourly from 2010 to 2013 for 1600 locations in the United States.

All of these datasets and their corresponding missing 10% to 80% of files are in the /data/ETT directory

Commands to test the imputation

python -u main_informer.py --model informer --data ETTh1 --do_predict --pred_len 24 --seq_len 48 --label_len 24 --itr 2

The detailed descriptions about the arguments are as following:

To change the dataset miss ratio, specify the dataset file by changing Data_Imputation in data_loader.py