# Model-selection-MHMMR In samurais: Statistical Models for the Unsupervised Segmentation of Time-Series ('SaMUraiS')

```library(knitr)
knitr::opts_chunk\$set(
fig.align = "center",
fig.height = 5.5,
fig.width = 6,
warning = FALSE,
collapse = TRUE,
dev.args = list(pointsize = 10),
out.width = "90%",
par = TRUE
)
knit_hooks\$set(par = function(before, options, envir)
{ if (before && options\$fig.show != "none")
par(family = "sans", mar = c(4.1,4.1,1.1,1.1), mgp = c(3,1,0), tcl = -0.5)
})
```
```library(samurais)
```

# Introduction

In this package, it is possible to select models based on information criteria such as BIC, AIC and ICL.

The selection is done on two parameters which are:

• \$K\$: The number of regimes;
• \$p\$: The order of the polyniomial regression.

# Data

Let's select a MHMMR model for the following multivariate time series \$Y\$:

```data("multivtoydataset")
x <- multivtoydataset\$x
y <- multivtoydataset[, c("y1", "y2", "y3")]
matplot(x, y, type = "l", xlab = "x", ylab = "Y")
```

# Model selection with BIC

```selectedmhmmr <- selectMHMMR(X = x, Y = y, Kmin = 2, Kmax = 6, pmin = 0, pmax = 3)
```

The selected model has \$K = 5\$ regimes and the order of the polynomial regression is \$p = 0\$. According to the way \$Y\$ has been generated, these parameters are what we expected.

Let's summarize the selected model:

```selectedmhmmr\$summary()
```
```selectedmhmmr\$plot(what = "smoothed")
```

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samurais documentation built on July 28, 2019, 5:02 p.m.