ada7b4b978
* add information changes * update typos * add typo changes * update documents * update chronos.md * add updates * fix rst * add speed-up md * fix broken links and typos, add tutorials
18 lines
1.3 KiB
Markdown
18 lines
1.3 KiB
Markdown
# Generate Synthetic Sequential Data Overview
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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 seperate 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 evalution 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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