Are models like TFT and N-BEATS considered generative for time-series forecasting If not any generative AI frameworks or tutorials in TensorFlow for time-series

Question

With the help of proper code example tell me Are models like TFT and N-BEATS considered generative for time-series forecasting? If not, any generative AI frameworks or tutorials in TensorFlow for time-series?

Answer 1

Models like TFT and N-BEATS are not generative but predictive; for generative AI in time-series forecasting, TensorFlow frameworks like GANs and VAEs can be used to generate synthetic time-series data.

Here is the code snippet you can refer to:

In the above code we are using the following key points:

• Implements a Simple GAN Architecture for generating synthetic time-series.
• Uses a Fully Connected Generator Network to produce sequences.
• Leverages Random Noise as Input for generating diverse outputs.
• Demonstrates TensorFlow’s Ability to create generative models for time-series.
• Can Be Extended with More Complex Architectures like Temporal GANs or VAEs

Hence, while TFT and N-BEATS are predictive, generative models like GANs and VAEs in TensorFlow can be used for synthetic time-series forecasting, aiding in data augmentation and anomaly detection.