feat: add doppelganger docs

Author: ricardodcpereira

README.md

@@ -120,6 +120,7 @@ In this repository you can find the several GAN architectures that are used to c
 
 ### Sequential data
   - [TimeGAN](https://papers.nips.cc/paper/2019/file/c9efe5f26cd17ba6216bbe2a7d26d490-Paper.pdf)
+  - [DoppelGANger](https://dl.acm.org/doi/pdf/10.1145/3419394.3423643)
 
 ## Contributing
 We are open to collaboration! If you want to start contributing you only need to:

docs/examples/doppelganger_example.md

@@ -0,0 +1,21 @@
+# Synthesize time-series data
+
+**Using *DoppelGANger* to generate synthetic time-series data:**
+
+Although tabular data may be the most frequently discussed type of data, a great number of real-world domains — from traffic and daily trajectories to stock prices and energy consumption patterns — produce **time-series data** which introduces several aspects of complexity to synthetic data generation.
+
+Time-series data is structured sequentially, with observations **ordered chronologically** based on their associated timestamps or time intervals. It explicitly incorporates the temporal aspect, allowing for the analysis of trends, seasonality, and other dependencies over time. 
+
+DoppelGANger is a model that uses a Generative Adversarial Network (GAN) framework to generate synthetic time series data by learning the underlying temporal dependencies and characteristics of the original data:
+
+- 📑 **Paper:** [Using GANs for Sharing Networked Time Series Data: Challenges, Initial Promise, and Open Questions](https://dl.acm.org/doi/pdf/10.1145/3419394.3423643)
+
+Here’s an example of how to synthetize time-series data with DoppelGANger using the [Yahoo Stock Price](https://www.kaggle.com/datasets/arashnic/time-series-forecasting-with-yahoo-stock-price) dataset:
+
+
+```python
+--8<-- "examples/timeseries/stock_doppelganger.py"
+```
+
+
+

docs/index.md

@@ -56,3 +56,4 @@ The following architectures are currently supported:
 - [CWGAN-GP](https://cameronfabbri.github.io/papers/conditionalWGAN.pdf) (Conditional Wassertein GAN with Gradient Penalty)
 - [CTGAN](https://arxiv.org/pdf/1907.00503.pdf) (Conditional Tabular GAN)
 - [TimeGAN](https://papers.nips.cc/paper/2019/file/c9efe5f26cd17ba6216bbe2a7d26d490-Paper.pdf) (specifically for *time-series* data)
+- [DoppelGANger](https://dl.acm.org/doi/pdf/10.1145/3419394.3423643) (specifically for *time-series* data)

docs/reference/api/synthesizers/timeseries/doppelganger.md

@@ -0,0 +1,2 @@
+
+::: ydata_synthetic.synthesizers.timeseries.doppelganger.model.DoppelGANger

examples/timeseries/stock_doppelganger.py

@@ -28,6 +28,7 @@
 else:
     model_dop_gan = TimeSeriesSynthesizer(modelname='doppelganger', model_parameters=model_args)
     model_dop_gan.fit(stock_data, train_args, num_cols=["Open", "High", "Low", "Close", "Adj_Close", "Volume"])
+    model_dop_gan.save('doppelganger_stock')
 
 # Generating new synthetic samples
 synth_data = model_dop_gan.sample(n_samples=500)

mkdocs.yml

@@ -21,6 +21,7 @@ nav:
         - CWGAN-GP: "examples/cwgangp_example.md"
       - Generate Time-Series Data:
         - TimeGAN: "examples/timegan_example.md"
+        - DoppelGANger: "examples/doppelganger_example.md"
       - Frequently Asked Questions: "examples/faqs.md"
   - Integrations:
       - Great Expectations: "integrations/gx_integration.md"
@@ -43,6 +44,7 @@ nav:
           - WGAN: 'reference/api/synthesizers/regular/wgan.md'
         - Timeseries:
           - TimeGAN: 'reference/api/synthesizers/timeseries/timegan.md'
+          - DoppelGANger: 'reference/api/synthesizers/timeseries/doppelganger.md'
       - Preprocessing:
         - BaseProcessor: 'reference/api/preprocessing/base.md'
         - Regular: