https://github.com/jianzhnie/tsformer

TsFormer is a toolbox that implement transformer models on Time series model
https://github.com/jianzhnie/tsformer

Last synced: 2 months ago
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TsFormer is a toolbox that implement transformer models on Time series model

• Host: GitHub
• URL: https://github.com/jianzhnie/tsformer
• Owner: jianzhnie
• License: apache-2.0
• Created: 2022-01-10T08:14:40.000Z (almost 4 years ago)
• Default Branch: main
• Last Pushed: 2024-07-25T11:13:59.000Z (about 1 year ago)
• Last Synced: 2025-04-03T07:51:23.545Z (7 months ago)
• Language: Python
• Size: 1.25 MB
• Stars: 11
• Watchers: 2
• Forks: 3
• Open Issues: 2
• Metadata Files:
  • Readme: README.md
  • License: LICENSE

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README

          

# TsFormer

TsFormer is a toolbox that implement transformer models on Time series data

## Todo

1. data preprocess

- gefcom2014

- uci

- ETT

2. dataloader

- dataloaders

3. models

- rnn

- lstm

- GRU

- ESN

- CNN

- TCN

- transformer

- informer

- autoformer

4. TODO

- [Spacetimeformer](https://github.com/QData/spacetimeformer)

- [SCINet](https://github.com/cure-lab/SCINet)

- [Deep learning for time series forecasting](https://github.com/AIStream-Peelout/flow-forecast)

- [PyTorch Forecasting](https://github.com/jdb78/pytorch-forecasting)

- [tsai](https://github.com/timeseriesAI/tsai)

- [flow-forecast](https://github.com/AIStream-Peelout/flow-forecast)

5. train and evaluate







## 6. Custom  Informer

```sh

python -u run_autoformer.py \

        --is_training 1 \

        --root_path ./data/electricity/ \

        --data_path electricity.csv \

        --model_id ECL \

        --model informer \

        --data custom \

        --features S \

        --seq_len 96 \

        --label_len 48 \

        --pred_len 96 \

        --e_layers 2 \

        --d_layers 1 \

        --factor 3 \

        --enc_in 1 \

        --dec_in 1 \

        --c_out 1 \

        --embed fixed \

        --des 'Exp' \

        --itr 1

mse:0.2755982279777527, mae:0.3857262134552002,rmse:0.524974524974823, mape:1.9572646617889404, mspe:238.20448303222656

```

```sh

python -u run_autoformer.py \

        --is_training 1 \

        --root_path ./data/electricity/ \

        --data_path electricity.csv \

        --model_id ECL \

        --model informer \

        --data custom \

        --features S \

        --seq_len 96 \

        --label_len 48 \

        --pred_len 96 \

        --e_layers 2 \

        --d_layers 1 \

        --factor 3 \

        --enc_in 1 \

        --dec_in 1 \

        --c_out 1 \

        --embed timeF \

        --des 'Exp' \

        --itr 1

mse:0.22287048399448395, mae:0.3356129825115204,rmse:0.4720916152000427, mape:1.6913783550262451, mspe:260.3700866699219

```

### Transformer Results

```sh

python -u run_autoformer.py \

        --is_training 1 \

        --root_path ./data/electricity/ \

        --data_path electricity.csv \

        --model_id ECL \

        --model transformer \

        --data custom \

        --features S \

        --seq_len 96 \

        --label_len 48 \

        --pred_len 96 \

        --e_layers 2 \

        --d_layers 1 \

        --factor 3 \

        --enc_in 1 \

        --dec_in 1 \

        --c_out 1 \

        --embed timeF \

        --des 'Exp' \

        --itr

mse:0.284598171710968, mae:0.38772597908973694,rmse:0.5334774255752563, mape:2.1156060695648193, mspe:381.4866943359375

```

## AutoFormer

```sh

python -u run_autoformer.py \

        --is_training 1 \

        --root_path ./data/electricity/ \

        --data_path electricity.csv \

        --model_id ECL \

        --model autoformer \

        --data custom \

        --features M \

        --seq_len 96 \

        --label_len 48 \

        --pred_len 96 \

        --e_layers 2 \

        --d_layers 1 \

        --factor 3 \

        --enc_in 321 \

        --dec_in 321 \

        --c_out 321 \

        --des 'Exp' \

        --itr 1

mse:0.2043592780828476, mae:0.3170555830001831,rmse:0.45206114649772644, mape:3.2521157264709473, mspe:414847.125

```