Generate Time Series Data Using AI
Using AI to Augment Your Time Series
Generative AI involves systems that can create new financial data or insights based on patterns they’ve learned from existing data.
Imagine it like this: you have a smart computer that learns from tons of historical financial data, like stock prices, market trends, and economic indicators. It then uses this knowledge to generate predictions or even create new financial scenarios.
Generative Models and Variational Autoencoders
Generative models are made to produce data that resembles a specified dataset. These models can generate new samples that are statistically similar to the training data by learning the underlying patterns, statistical distributions, or structures present in the data. Applications for generative models include data augmentation, text generation, and image generation.
Generative models include variational autoencoders (VAE). It is a probabilistic and generative model that adds encoding and decoding to the idea of conventional autoencoders. The following is how VAEs carry out their duties:
- An input data point is passed via the encoder, which converts it to a probability distribution in a smaller latent space. You may think of the encoder as a…
