documentation.md

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title	Documentation
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Tutorials

If you are interested, please follow our Medium channel to get the latest blog posts.

• Numpy in Python
  • Generating Random Numbers and Arrays in Matlab and Numpy
• Matrix computations
  • Understanding Lyapunov Equation through Kronecker Product and Linear Equation
  • Intuitive Understanding of Randomized Singular Value Decomposition
• Autoregressive models
  • Matrix Autoregressive Model for Multidimensional Time Series Forecasting
  • Reduced-Rank Vector Autoregressive Model for High-Dimensional Time Series Forecasting
• Dynamic mode decomposition
  • Dynamic Mode Decomposition for Multivariate Time Series Forecasting
  • Dynamic Mode Decomposition for Spatiotemporal Traffic Speed Time Series in Seattle Freeway

Toy Examples

• [E1] Bayesian vector autoregressive forecasting
  • About this notebook:
    • The basics of vector autoregressive model.
    • Use Gaussian-Wishart distribution as a conjugate prior in Bayesian vector autoregressive (BVAR) model.
    • Implement the Gibbs sampling algorithm for BVAR in Python.
    • Perform multivariate time series forecasting using BVAR.
  • Highlight:
    • Uncertainty estimation.