Are Transformers Effective for Time Series Forecasting?

Are Transformers Effective for Time Series Forecasting?

This is a warehouse for DLinear-Pytorch-model, can be used to train your text dataset for time series forecasting tasks.
Code mainly from official source code

Preparation

Install Packages & Configuration

Download the dataset:

TimeSeriesDatasets Nine Benchmarks.

Project Structure

├── datasets: Load datasets
    ├── data_factory.py: build train/val/test dataset
    ├── mydataset.py: Customize reading data sets and define transforms data enhancement methods
├── models: DLinear Model
    ├── DLinear_model.py: Construct DLinear model
    ├── Reformer_model.py: Construct Reformer model
    ├── AutoFormer_model.py: Construct AutoFormer model
    ├── Informer_model.py: Construct Informer model
    ├── FEDFormer_model.py: Construct FEDFormer model
    ├── Transformer_model.py: Construct Transformer model
    ├── NLinear_model.py: Construct NLinear model
    ├── Linear_model.py: Construct Linear model
    ├── ...: essential blocks(branches) for each model
├── util:
    ├── engine.py: Function code for a training/validation process
    ├── losses.py: RMSE Loss
    ├── lr_sched.py: create_lr_scheduler
    ├── masking.py: Construct TriangularCausalMask & ProbMask
    ├── optimizer.py: finetune Lion optimizer
    ├── samplers.py: Define the parameter of "sampler" in DataLoader
    ├── time_features.py: build time feature data (year, month, hour)
    ├── tools.py: Define help functions (adjust_learning_rate, visual, stanstardscaler, EarlyStopping, etc.)
    ├── utils.py: Record various indicator information and output and distributed environment
└── train_gpu.py: Training model startup file (including infer process)

Precautions

Before you use the code to train your own data set, please first enter the train_gpu.py file and modify the data_root, batch_size, num_workers and nb_classes parameters.

Train this model

Parameters Meaning:

1. nproc_per_node: <The number of GPUs you want to use on each node (machine/server)>
2. CUDA_VISIBLE_DEVICES: <Specify the index of the GPU corresponding to a single node (machine/server) (starting from 0)>
3. nnodes: <number of nodes (machine/server)>
4. node_rank: <node (machine/server) serial number>
5. master_addr: <master node (machine/server) IP address>
6. master_port: <master node (machine/server) port number>

Note:

If you want to use multiple GPU for training, whether it is a single machine with multiple GPUs or multiple machines with multiple GPUs, each GPU will divide the batch_size equally. For example, batch_size=4 in my train_gpu.py. If I want to use 2 GPUs for training, it means that the batch_size on each GPU is 4. Do not let batch_size=1 on each GPU, otherwise BN layer maybe report an error.

train model with single-machine single-GPU：

python train_gpu.py

train model with single-machine multi-GPU：

python -m torch.distributed.run --nproc_per_node=8 train_gpu.py

train model with single-machine multi-GPU:

(using a specified part of the GPUs: for example, I want to use the second and fourth GPUs)

CUDA_VISIBLE_DEVICES=1,3 python -m torch.distributed.run --nproc_per_node=2 train_gpu.py

train model with multi-machine multi-GPU:

(For the specific number of GPUs on each machine, modify the value of --nproc_per_node. If you want to specify a certain GPU, just add CUDA_VISIBLE_DEVICES= to specify the index number of the GPU before each command. The principle is the same as single-machine multi-GPU training)

On the first machine: python -m torch.distributed.run --nproc_per_node=1 --nnodes=2 --node_rank=0 --master_addr=<Master node IP address> --master_port=<Master node port number> train_gpu.py

On the second machine: python -m torch.distributed.run --nproc_per_node=1 --nnodes=2 --node_rank=1 --master_addr=<Master node IP address> --master_port=<Master node port number> train_gpu.py

Citation

@inproceedings{Zeng2022AreTE,
  title={Are Transformers Effective for Time Series Forecasting?},
  author={Ailing Zeng and Muxi Chen and Lei Zhang and Qiang Xu},
  journal={Proceedings of the AAAI Conference on Artificial Intelligence},
  year={2023}
}