---
title: "Introduction"
output: rmarkdown::html_vignette
vignette: >
  %\VignetteIndexEntry{Introduction}
  %\VignetteEngine{knitr::rmarkdown}
  %\VignetteEncoding{UTF-8}
---

```{r, include = FALSE}
knitr::opts_chunk$set(
  collapse = TRUE,
  comment = "#>"
)
```

```{r setup}

```

StructuralDecompose is a method that breaks a time series algorithm into various parts. It is particularly well suited to a time series that has several level shifts within it. 

StructuralDecompose returns the series constituent parts including its Trend, Seasonality and residuals. As well as a fairly inbuilt summary of the time series itself and how well it has fit the data. As it performs inbuilt Anomaly Detection, it also returns a series of points that it considers to be anomalies. However more advanced anomaly detection techniques should be considered if you are doing anomaly detection on the time series. 


