900_timeSeries.Rmd

# Time Series


Many sources of information are time related. For example, data from Software Configuration Management (SCM) such as Git, [GitHub](http://www.github.com)) systems or Dashboards such as [Metrics Grimoire](http://metricsgrimoire.github.io/) from [Bitergia](http://bitergia.com/) or [SonarQube](http://www.sonarqube.org/) 


With MetricsGrimore or SonarQube we can extract datasets or dump of databases. For example, a dashboard for the OpenStack project is located at [http://activity.openstack.org/dash/browser/](http://activity.openstack.org/dash/browser/) and provides datasets as MySQL dumps or JSON files.

With R we can read a JSON file as follows:

```{r}
library(jsonlite)
# Get the JSON data 
# gm <- fromJSON("http://activity.openstack.org/dash/browser/data/json/nova.git-scm-rep-evolutionary.json")
gm <- fromJSON('./datasets/timeSeries/nova.git-scm-rep-evolutionary.json')
str(gm)
```

Now we can use time series packages. First, after loading the libraries, we need to create a time series object.

```{r}
# TS libraries
library(xts)
library(forecast)

# Library to deal with dates
library(lubridate)

# Ceate a time series object
gmts <- xts(gm$commits,seq(ymd('2010-05-22'),ymd('2015-11-16'), by = '1 week'))

# TS Object
str(gmts)

head(gmts, 3)
```


Visualise the time series object
```{r}
plot(gmts)
```

Arima model:
```{r}
fit <- auto.arima(gmts)
fit
```

```{r}
forecast(fit, 5)
plot(forecast(fit, 5))
```

## Web tutorials about Time Series:

http://www.statoek.wiso.uni-goettingen.de/veranstaltungen/zeitreihen/sommer03/ts_r_intro.pdf

http://www.statmethods.net/advstats/timeseries.html

http://a-little-book-of-r-for-time-series.readthedocs.org/en/latest/

https://media.readthedocs.org/pdf/a-little-book-of-r-for-time-series/latest/a-little-book-of-r-for-time-series.pdf

http://www.stat.pitt.edu/stoffer/tsa3/