{ "cells": [ { "cell_type": "markdown", "metadata": {}, "source": [ "# Bayesian Temporal Regularized Tensor Factorization\n", "\n", "**Published**: December 27, 2020\n", "\n", "**Author**: Xinyu Chen [[**GitHub homepage**](https://github.com/xinychen)]\n", "\n", "**Download**: This Jupyter notebook is at our GitHub repository. If you want to evaluate the code, please download the notebook from the [**transdim**](https://github.com/xinychen/transdim/blob/master/predictor/BTRTF.ipynb) repository.\n", "\n", "This notebook shows how to implement the Bayesian Temporal Regularized Tensor Factorization (BTRTF), a fully Bayesian matrix factorization model, on some real-world data sets. To overcome the missing data problem in multivariate time series, BTRTF takes into account both low-rank matrix structure and time series autoregression. For an in-depth discussion of BTRTF, please see [1].\n", "\n", "