ETSformer-pytorch

ETSformer - Pytorch

在Pytorch中实现ETSformer，一种最先进的时间序列Transformer

安装

$ pip install etsformer-pytorch

使用方法

import torch
from etsformer_pytorch import ETSFormer

model = ETSFormer(
    time_features = 4,
    model_dim = 512,                # 论文中使用512
    embed_kernel_size = 3,          # 用于输入嵌入的一维卷积核大小
    layers = 2,                     # 编码器和对应解码器的层数
    heads = 8,                      # 指数平滑注意力头的数量
    K = 4,                          # 保留振幅最高的频率数量（进行注意力计算）
    dropout = 0.2                   # dropout（论文中使用0.2）
)

timeseries = torch.randn(1, 1024, 4)

pred = model(timeseries, num_steps_forecast = 32) # (1, 32, 4) - (批次, 预测步数, 时间特征数)

使用ETSFormer进行分类，对所有潜在变量和水平输出进行交叉注意力池化

import torch
from etsformer_pytorch import ETSFormer, ClassificationWrapper

etsformer = ETSFormer(
    time_features = 1,
    model_dim = 512,
    embed_kernel_size = 3,
    layers = 2,
    heads = 8,
    K = 4,
    dropout = 0.2
)

adapter = ClassificationWrapper(
    etsformer = etsformer,
    dim_head = 32,
    heads = 16,
    dropout = 0.2,
    level_kernel_size = 5,
    num_classes = 10
)

timeseries = torch.randn(1, 1024)

logits = adapter(timeseries) # (1, 10)

引用

@misc{woo2022etsformer,
    title   = {ETSformer: Exponential Smoothing Transformers for Time-series Forecasting}, 
    author  = {Gerald Woo and Chenghao Liu and Doyen Sahoo and Akshat Kumar and Steven Hoi},
    year    = {2022},
    eprint  = {2202.01381},
    archivePrefix = {arXiv},
    primaryClass = {cs.LG}
}