README.md
In edxu96/MatrixTSA: Tidy Multivariate (Non)Linear Dynamic Systems

tidynamics

Tidy Analysis of Multivariate (Non)-Linear Dynamic Systems

For those who are not statisticians, like economists and traders, a well-established collections of statistical tools is needed, and this package is my toolbox to model dynamics in renewable energy systems and markets. You can also try to model other systems.

R packages are (after a short learning phase) a comfortable way to maintain collections of R functions and data sets. As an article distributes scientific ideas to others, a package distributes statistical methodology to others. Most users first see the packages of functions distributed with R or from CRAN. The package system allows many more people to contribute to R while still enforcing some standards. But packages are also a convenient way to maintain private functions and share them with your colleagues. I have a private package of utility function, my working group has several “experimental” packages where we try out new things. This provides a transparent way of sharing code with co-workers, and the final transition from “playground” to production code is much easier. ("Creating R Packages: A Tutorial", Friedrich Leisch)

Modern statistics in R.

The tidyverse is an opinionated collection of R packages designed for data science. All packages share an underlying design philosophy, grammar, and data structures. (tidyverse)

[x] tidyverse Favored Grammar
[ ] Adaptivity
[ ] Based on Likelihood Theory
[ ] Matrix Computation
[x] Functionals and Structs

install.packages(pkgs = tidynamics, repos = https://github.com/edxu96/tidynamics.git)

> devtools::install_github("edxu96/tidynamics")

The input and output from observations of some dynamic process can always be combined to a matrix, which we call mat_oi. For outputs with uni-variate time series, there are only two columns in the matrix. For those with multivariate time series, it's convenient to see the data in matrix.

The following categories of models will be included in this package.

| Method | Static / Dynamic | Linear / Non-Linear | | ----------------------- | ---------------- | ------------------- | | Linear Regression | Static | Linear | | Linear Additive Decomp. | Static | Linear | | Generalized Additive M. | Static | Non-Linear | | ARIMA (without input) | Dynamic | Linear | | Input-Output Model | Dynamic | Linear | | Linear State Space M. | Dynamic | Linear | | Stochastic Diff. Eq. | Dynamic | Non-Linear | | Tree-Based M. | Static | Non-Linear |

Well-Defined Data from Physical Systems

Prediction -> Supervised Learning, High Accuracy
- Stochastic Programming
- Dynamic Programming
- Model Predictive Control
Inference -> Supervised Learning, High Interpretability
- Science Research
Summarize -> Unsupervised Learning
- Visualization
- Group