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you-n-g cf35562e84 DDG-DA paper code (#743) 
* Merge data selection to main

* Update trainer for reweighter

* Typos fixed.

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* successfully run exp after refactor some interface

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* fix meta model time series bug

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Co-authored-by: wendili-cs <wendili.academic@qq.com>
Co-authored-by: demon143 <785696300@qq.com>
2022-01-10 16:52:37 +08:00
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data_formatters
fix_typo (#790)
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expt_settings
Refactor to Python3 style
2020-12-15 20:37:43 +08:00
libs
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README.md
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requirements.txt
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tft.py
DDG-DA paper code (#743)
2022-01-10 16:52:37 +08:00
workflow_config_tft_Alpha158.yaml
simplify signal parameter
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README.md

Temporal Fusion Transformers Benchmark

Source

Reference: Lim, Bryan, et al. "Temporal fusion transformers for interpretable multi-horizon time series forecasting." arXiv preprint arXiv:1912.09363 (2019).

GitHub: https://github.com/google-research/google-research/tree/master/tft

Run the Workflow

Users can follow the workflow_by_code_tft.py to run the benchmark.

Notes

  1. Please be aware that this script can only support Python 3.6 - 3.7.
  2. If the CUDA version on your machine is not 10.0, please remember to run the following commands conda install anaconda cudatoolkit=10.0 and conda install cudnn on your machine.
  3. The model must run in GPU, or an error will be raised.
  4. New datasets should be registered in data_formatters, for detail please visit the source.