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src=\"./etsformer.png\" width=\"400px\"\u003e\u003c/img\u003e\n\n## ETSformer - Pytorch\n\nImplementation of \u003ca href=\"https://arxiv.org/abs/2202.01381\"\u003eETSformer\u003c/a\u003e, state of the art time-series Transformer, in Pytorch\n\n## Install\n\n```bash\n$ pip install etsformer-pytorch\n```\n\n## Usage\n\n```python\nimport torch\nfrom etsformer_pytorch import ETSFormer\n\nmodel = ETSFormer(\n    time_features = 4,\n    model_dim = 512,                # in paper they use 512\n    embed_kernel_size = 3,          # kernel size for 1d conv for input embedding\n    layers = 2,                     # number of encoder and corresponding decoder layers\n    heads = 8,                      # number of exponential smoothing attention heads\n    K = 4,                          # num frequencies with highest amplitude to keep (attend to)\n    dropout = 0.2                   # dropout (in paper they did 0.2)\n)\n\ntimeseries = torch.randn(1, 1024, 4)\n\npred = model(timeseries, num_steps_forecast = 32) # (1, 32, 4) - (batch, num steps forecast, num time features)\n```\n\nFor using ETSFormer for classification, using cross attention pooling on all latents and level output\n\n```python\nimport torch\nfrom etsformer_pytorch import ETSFormer, ClassificationWrapper\n\netsformer = ETSFormer(\n    time_features = 1,\n    model_dim = 512,\n    embed_kernel_size = 3,\n    layers = 2,\n    heads = 8,\n    K = 4,\n    dropout = 0.2\n)\n\nadapter = ClassificationWrapper(\n    etsformer = etsformer,\n    dim_head = 32,\n    heads = 16,\n    dropout = 0.2,\n    level_kernel_size = 5,\n    num_classes = 10\n)\n\ntimeseries = torch.randn(1, 1024)\n\nlogits = adapter(timeseries) # (1, 10)\n```\n\n## Citation\n\n```bibtex\n@misc{woo2022etsformer,\n    title   = {ETSformer: Exponential Smoothing Transformers for Time-series Forecasting}, \n    author  = {Gerald Woo and Chenghao Liu and Doyen Sahoo and Akshat Kumar and Steven Hoi},\n    year    = {2022},\n    eprint  = {2202.01381},\n    archivePrefix = {arXiv},\n    primaryClass = {cs.LG}\n}\n```\n","project_url":"https://awesome.ecosyste.ms/api/v1/projects/github.com%2Flucidrains%2FETSformer-pytorch","html_url":"https://awesome.ecosyste.ms/projects/github.com%2Flucidrains%2FETSformer-pytorch","lists_url":"https://awesome.ecosyste.ms/api/v1/projects/github.com%2Flucidrains%2FETSformer-pytorch/lists"}