Predicting sales across different items/stores using multi task gp model with shared parameters is fairly limited. Gaussian Process Regression Networks should help in modelling correlations between items/stores.
Test data: Walmart umbrella competition.
To read:
Multi output GP - shared covariance function on input-dependent features
Convolved GPs - Convolving kernel functions for multiple outputs with Gaussian white noise allows for obtaining a positive semidefinite covariance matrix
Background:
Predicting sales across different items/stores using multi task gp model with shared parameters is fairly limited. Gaussian Process Regression Networks should help in modelling correlations between items/stores.
Test data: Walmart umbrella competition.
To read:
Multi output GP - shared covariance function on input-dependent features
Correlated outputs via a linear combination of GPs (fixed W coefficient matrix for combining multiple GPs)
GPRN - Correlation between multiple outputs are adaptive. Every cell of W matrix for mixing multiple GPs is a Gaussian Process itself.
Convolved GPs - Convolving kernel functions for multiple outputs with Gaussian white noise allows for obtaining a positive semidefinite covariance matrix
Variational inference + Stochastic variational inference
Other multi output algorithms for non GP models