Update README.md

README.md

@@ -125,16 +125,16 @@ TSGM implements several generative models for synthetic time series data.
 
 | Method  | Link to docs | Type | Notes |
 | ------------- | ------------- | ------------- | ------------- |
-| Structural Time Series model  | [tsgm.models.sts.STS](https://tsgm.readthedocs.io/en/latest/modules/root.html#tsgm.models.sts.STS) | Data-driven | Great for modeling time series when prior knowledge is available (e.g., trend or seasonality).  |        
-| GAN  | [tsgm.models.cgan.GAN](https://tsgm.readthedocs.io/en/latest/modules/root.html#tsgm.models.cgan.GAN) | Data-driven | A generic implementation of GAN for time series generation. It can be customized with architectures for generators and discriminators. |
-| WaveGAN  | [tsgm.models.cgan.GAN](https://tsgm.readthedocs.io/en/latest/modules/root.html#tsgm.models.cgan.GAN) | Data-driven | WaveGAN is the model for audio synthesis proposed in [Adversarial Audio Synthesis](https://arxiv.org/abs/1802.04208). To use WaveGAN, set `use_wgan=True` when initializing the GAN class and use the `zoo["wavegan"]` architecture from the model zoo. |
-| ConditionalGAN | [tsgm.models.cgan.ConditionalGAN](https://tsgm.readthedocs.io/en/latest/modules/root.html#tsgm.models.cgan.ConditionalGAN) | Data-driven | A generic implementation of conditional GAN. It supports scalar conditioning as well as temporal one. |
-| BetaVAE  | [tsgm.models.cvae.BetaVAE](https://tsgm.readthedocs.io/en/latest/modules/root.html#tsgm.models.cvae.BetaVAE) | Data-driven | A generic implementation of Beta VAE for TS. The loss function is customized to work well with multi-dimensional time series. |
-| cBetaVAE  | [tsgm.models.cvae.cBetaVAE](https://tsgm.readthedocs.io/en/latest/modules/root.html#tsgm.models.cvae.cBetaVAE) | Data-driven | Conditional version of BetaVAE. It supports temporal a scalar condiotioning.|
-| TimeGAN | [tsgm.models.timegan.TimeGAN](https://tsgm.readthedocs.io/en/latest/modules/root.html#tsgm.models.timegan.TimeGAN) | Data-driven | TSGM implementation of TimeGAN from [paper](https://papers.nips.cc/paper_files/paper/2019/hash/c9efe5f26cd17ba6216bbe2a7d26d490-Abstract.html) |
-| SineConstSimulator | [tsgm.simulator.SineConstSimulator](https://tsgm.readthedocs.io/en/latest/modules/root.html#tsgm.simulator.SineConstSimulator) | Simulator-based | Simulator-based synthetic signal that switches between constant and periodics functions. |
-| LotkaVolterraSimulator | [tsgm.simulator.LotkaVolterraSimulator](https://tsgm.readthedocs.io/en/latest/modules/root.html#tsgm.simulator.LotkaVolterraSimulator) | Simulator-based | Simulator-based synthetic signal that switches between constant and periodics functions. |
-| PredictiveMaintenanceSimulator | [tsgm.simulator.PredictiveMaintenanceSimulator](https://tsgm.readthedocs.io/en/latest/modules/root.html#tsgm.simulator.PredictiveMaintenanceSimulator) | Simulator-based | Simulator of predictive maintenance with multiple pieces of equipment from [paper](https://arxiv.org/pdf/2206.11574) |
+| Structural Time Series  | [sts.STS](https://tsgm.readthedocs.io/en/latest/modules/root.html#tsgm.models.sts.STS) | Data-driven | Great for modeling time series when prior knowledge is available (e.g., trend or seasonality).  |        
+| GAN  | [GAN](https://tsgm.readthedocs.io/en/latest/modules/root.html#tsgm.models.cgan.GAN) | Data-driven | A generic implementation of GAN for time series generation. It can be customized with architectures for generators and discriminators. |
+| WaveGAN  | [GAN](https://tsgm.readthedocs.io/en/latest/modules/root.html#tsgm.models.cgan.GAN) | Data-driven | WaveGAN is the model for audio synthesis proposed in [Adversarial Audio Synthesis](https://arxiv.org/abs/1802.04208). To use WaveGAN, set `use_wgan=True` when initializing the GAN class and use the `zoo["wavegan"]` architecture from the model zoo. |
+| ConditionalGAN | [ConditionalGAN](https://tsgm.readthedocs.io/en/latest/modules/root.html#tsgm.models.cgan.ConditionalGAN) | Data-driven | A generic implementation of conditional GAN. It supports scalar conditioning as well as temporal one. |
+| BetaVAE  | [BetaVAE](https://tsgm.readthedocs.io/en/latest/modules/root.html#tsgm.models.cvae.BetaVAE) | Data-driven | A generic implementation of Beta VAE for TS. The loss function is customized to work well with multi-dimensional time series. |
+| cBetaVAE  | [cBetaVAE](https://tsgm.readthedocs.io/en/latest/modules/root.html#tsgm.models.cvae.cBetaVAE) | Data-driven | Conditional version of BetaVAE. It supports temporal a scalar condiotioning.|
+| TimeGAN | [TimeGAN](https://tsgm.readthedocs.io/en/latest/modules/root.html#tsgm.models.timegan.TimeGAN) | Data-driven | TSGM implementation of TimeGAN from [paper](https://papers.nips.cc/paper_files/paper/2019/hash/c9efe5f26cd17ba6216bbe2a7d26d490-Abstract.html) |
+| SineConstSimulator | [SineConstSimulator](https://tsgm.readthedocs.io/en/latest/modules/root.html#tsgm.simulator.SineConstSimulator) | Simulator-based | Simulator-based synthetic signal that switches between constant and periodics functions. |
+| Lotka Volterra | [LotkaVolterraSimulator](https://tsgm.readthedocs.io/en/latest/modules/root.html#tsgm.simulator.LotkaVolterraSimulator) | Simulator-based | Simulator-based synthetic signal that switches between constant and periodics functions. |
+| PdM Simulator | [PdMSimulator](https://tsgm.readthedocs.io/en/latest/modules/root.html#tsgm.simulator.PredictiveMaintenanceSimulator) | Simulator-based | Simulator of predictive maintenance with multiple pieces of equipment from [paper](https://arxiv.org/pdf/2206.11574) |
 
 ## :chart_with_upwards_trend: Metrics
 TSGM implements many metrics for synthetic time series evaluation. Check Section 3 from [our paper for more detail on the evaluation of synthetic time series](https://arxiv.org/pdf/2305.11567).