6e10d98a8d
* add llm-ppl workflow * update the DATASET_DIR * test multiple precisions * modify nightly test * match the updated ppl code * add matrix.include * fix the include error * update the include * add more model * update the precision of include * update nightly time and add more models * fix the workflow_dispatch description, change default model of pr and modify the env * modify workflow_dispatch language options * modify options * modify language options * modeify workflow_dispatch type * modify type * modify the type of language * change seq_len type * fix some typos * revert changes to stress_test.txt
18 lines
1.3 KiB
Markdown
18 lines
1.3 KiB
Markdown
# Synthetic Data Generation
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Chronos provides simulators to generate synthetic time series data for users who want to conquer limited data access in a deep learning/machine learning project or only want to generate some synthetic data to play with.
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```eval_rst
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.. note::
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``DPGANSimulator`` is the only simulator chronos provides at the moment, more simulators are on their way.
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```
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## 1. DPGANSimulator
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`DPGANSimulator` adopt DoppelGANger raised in [Using GANs for Sharing Networked Time Series Data: Challenges, Initial Promise, and Open Questions](http://arxiv.org/abs/1909.13403). The method is data-driven unsupervised method based on deep learning model with GAN (Generative Adversarial Networks) structure. The model features a pair of separate attribute generator and feature generator and their corresponding discriminators `DPGANSimulator` also supports a rich and comprehensive input data (training data) format and outperform other algorithms in many evaluation metrics.
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```eval_rst
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.. note::
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We reimplement this model by pytorch(original implementation was based on tf1) for better performance(both speed and memory).
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```
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Users may refer to detailed [API doc](../../PythonAPI/Chronos/simulator.html#module-bigdl.chronos.simulator.doppelganger_simulator).
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