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Autoregressive Models

(Stanford University - Jaclyn Chen)


- Overview

Autoregressive models are a type of statistical model that predicts future values in a time series based on its past values. Autoregressive models are used in statistics, econometrics, and signal processing to describe time-varying processes.

Autoregressive models are regression models that use lag series generated from the original time series. They are used to predict future values based on past values. They are often used in technical analysis to forecast future security prices. 

Autoregressive models assume that the future will be similar to the past. They are used when there is a correlation between the time series values and their preceding and succeeding values.


- Autoregressive Models in Generative AI

Autoregressive models assume that the value of a variable at a given time step is a linear combination of its past values. This assumption allows the model to learn the underlying structure of the data and generate new data points that follow the same distribution. 

In generative AI, autoregressive models are used to generate sequences of data points, such as text, images, or time series, by predicting one data point at a time, conditioning on the previously generated data points.

In generative AI, autoregressive models are used to generate new data points that follow the same distribution as the training data. 

Autoregressive models generate data one element at a time. They predict the probability distribution of the next element given the context of the previous elements and then sample from that distribution to generate new data.

Autoregressive models are a class of machine learning (ML) models that automatically predict the next component in a sequence by taking measurements from previous inputs in the sequence. 

Here are some other types of generative AI models:

  • Variational AutoEncoders (VAEs): A type of generative model that combines elements of both autoencoders and probabilistic models. VAEs are trained to encode input data into a lower-dimensional latent space and then decode it back into the original data space.
  • Convolutional Neural Networks (CNNs): A type of neural network designed to work with image data. CNNs use a series of convolutional layers to extract features from the input image and then use these features to make predictions.
  • Boltzmann machines: Generative models that can generate new data samples by sampling from their learned probability distribution. They are useful for various applications, such as image and speech recognition, anomaly detection, and recommendation systems.


[More to come ...]

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