Generative AI For Finance 101

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:

1. 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 recognition network. It comprises of one or more neural network layers that translate the input data into vectors representing the mean and variance of a multivariate Gaussian distribution and the parameters of that distribution in latent space.
2. Data is then represented in what is known as the latent space. The latent space captures the underlying structure of the data. Instead of directly sampling from the Gaussian distribution in the latent space, the VAE samples from a standard Gaussian distribution (with mean 0 and variance 1) and then scales and shifts the samples using the mean and variance from the encoder.
3. The latent space is then used to represent the data. The underlying structure of the data is captured by the latent space. The VAE samples from a conventional Gaussian distribution (with mean 0 and variance 1) instead of directly taking samples from the Gaussian distribution in the latent space. The samples are then scaled and shifted…