Temporal Patterns Decomposition and Legendre Projection for Long-term Time Series Forecasting.

1.Install Python 3.6, PyTorch 1.9.0.
2.Download data. You can obtain all the six benchmarks from Google Drive. All the datasets are well pre-processed and can be used easily.
3.Train the model. You can reproduce the experiment results by:

python -u run_longExp.py \
  --is_training 1 \
  --root_path ./dataset/ \
  --data_path ETTm2.csv \
  --model_id ETTm2_$seq_len'_'96 \
  --model $model_name \
  --data ETTm2 \
  --features M \
  --seq_len $seq_len \
  --pred_len 96 \
  --enc_in 7 \
  --des 'Exp' \
  --itr 1 --batch_size 32 --learning_rate 0.001 >logs/LongForecasting/$model_name'_'ETTm2_$seq_len'_'96.log

• numpy
• matplotlib
• pandas
• scikit-learn
• torch==1.9.0

• MICN
• InParformer
• CLformer
• FEDformer
• Autoformer
• Informer
• LogTrans
• Reformer
• LSTNet

https://github.com/zhouhaoyi/Informer2020
https://github.com/thuml/Autoformer
https://github.com/zhouhaoyi/ETDataset
https://github.com/laiguokun/multivariate-time-series-data