Automatic Generation of Probabilistic Programming from Time Series Data
This work addresses the challenge of model structure discovery in probabilistic programming for time series analysis, representing an incremental advance by automating program generation from data.
The paper tackles the problem of automatically generating probabilistic programs from continuous time series data when the model structure is unknown, using nonparametric Gaussian process regression to find descriptive covariance structures, and reports that this approach efficiently derives accurate probabilistic programming descriptions.
Probabilistic programming languages represent complex data with intermingled models in a few lines of code. Efficient inference algorithms in probabilistic programming languages make possible to build unified frameworks to compute interesting probabilities of various large, real-world problems. When the structure of model is given, constructing a probabilistic program is rather straightforward. Thus, main focus have been to learn the best model parameters and compute marginal probabilities. In this paper, we provide a new perspective to build expressive probabilistic program from continue time series data when the structure of model is not given. The intuition behind of our method is to find a descriptive covariance structure of time series data in nonparametric Gaussian process regression. We report that such descriptive covariance structure efficiently derives a probabilistic programming description accurately.