README.Rmd

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output: github_document
---

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```{r, include = FALSE}
knitr::opts_chunk$set(
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<img src="hex/hexsticker.png" align="right" height="200"/>

# tsdataleaks

![CRAN status](https://www.r-pkg.org/badges/version/tsdataleaks)](https://CRAN.R-project.org/package=tsdataleaks)



R Package for detecting data leakages in time series forecasting competitions.


<img src="paper/poster.png" align="center"/>

## Installation

<!--You can install the released version of tsdataleaks from --> <!-- [CRAN](https://CRAN.R-project.org) with: -->


<!--

``` r
install.packages("tsdataleaks")
```

-->



The development version from [GitHub](https://github.com/) with:


```r
install.packages("tsdataleaks")
library(tsdataleaks)
```

or


``` r
# install.packages("devtools")
devtools::install_github("thiyangt/tsdataleaks")
library(tsdataleaks)
```
## Example


To demonstrate the package functions, I created a small data set with 4 time series. 

```{r example, comment=NA, warning=FALSE, message=FALSE}
set.seed(2020)
a <- rnorm(15)
d <- rnorm(10)
lst <- list(
  a = a,
  b = c(a[10:15]+rep(8,6), rnorm(10), a[1:5], a[1:5]),
  c = c(rnorm(10), -a[1:5]),
  d = d,
  e = d)


```


## `find_dataleaks`: Exploit data leaks

```{r, comment=NA, message=FALSE, warning=FALSE}
library(tsdataleaks)
library(magrittr)
library(tidyverse)
library(viridis)
# h - I assume test period length is 5 and took that as wind size, h.
f1 <- find_dataleaks(lstx = lst, h=5, cutoff=1) 
f1
```

Interpretation: The first element in the list means the last 5 observations of the time series `a` correlates with time series `b` observarion from 2 to 6.

## `viz_dataleaks`: Visualise the data leaks

```{r, comment=NA, message=FALSE, warning=FALSE}
viz_dataleaks(f1)
```

## `reason_dataleaks`

Display the reasons for data leaks and evaluate usefulness of data leaks towards the winning of the competition

```{r, comment=NA, message=FALSE, warning=FALSE}
r1 <- reason_dataleaks(lstx = lst, finddataleaksout = f1, h=5)
r1
```

# A list without naming element 

```{r, warning=FALSE, message=FALSE}
a = rnorm(15)
lst <- list(
  a,
  c(a[10:15], rnorm(10), a[1:5], a[1:5]),
  c(rnorm(10), a[1:5])
)
f1 <- find_dataleaks(lst, h=5)
```

```{r, warning=FALSE, message=FALSE}
viz_dataleaks(f1)
```

```{r, warning=FALSE, message=FALSE}
reason_dataleaks(lst, f1, h=5)
```


# Application to M-Competition data

## M1 Competition - Yearly data

```{r, warning=FALSE, message=FALSE}
library(Mcomp)
data("M1")
M1Y <- subset(M1, "yearly")
M1Y_x <- lapply(M1Y, function(temp){temp$x})
m1y_f1 <- find_dataleaks(M1Y_x, h=6, cutoff = 1)
m1y_f1
```

```{r, warning=FALSE, message=FALSE}
viz_dataleaks(m1y_f1)
```

```{r, warning=FALSE, message=FALSE, fig.width=12}
reason_dataleaks(M1Y_x, m1y_f1, h=6, ang=90)
```