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README.rmd
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---
title: TimeSeries.OBeu <img src="okfgr2.png" align="right" />
author: "Kleanthis Koupidis, Charalampos Bratsas"
output:
github_document:
fig_width: 8
fig_height: 8
dev: png
html_preview: no
number_sections: yes
---
[](https://github.com/okgreece/TimeSeries.OBeu/actions)
[](https://cran.r-project.org/package=TimeSeries.OBeu)
[](http://www.repostatus.org/#active)
[](http://cran.rstudio.com/web/packages/TimeSeries.OBeu/index.html)
[](https://www.gnu.org/licenses/old-licenses/gpl-2.0.html)
[](https://zenodo.org/badge/latestdoi/69395698)
#[TimeSeries.OBeu ](https://okgreece.github.io/TimeSeries.OBeu/)
Εstimate and return the necessary parameters for time series visualizations, used in [OpenBudgets.eu](http://openbudgets.eu/). It includes functions to test stationarity (with ACF, PACF, Phillips Perron test, Augmented Dickey Fuller (ADF) test, Kwiatkowski-Phillips-Schmidt-Shin (KPSS) test, Mann Kendall Test For Monotonic Trend and Cox and Stuart trend test), decompose, model and forecast Budget time series data of municipalities across Europe, according to the [OpenBudgets.eu data model](https://github.com/openbudgets/data-model).
This package can generally be used to extract visualization parameters convert them to JSON format and use them as input in a different graphical interface. Most functions can have general use out of the [OpenBudgets.eu data model](https://github.com/openbudgets/data-model).
You can see detailed information [here](https://okgreece.github.io/TimeSeries.OBeu/).
```{r, eval=FALSE, include=TRUE}
# install TimeSeries.OBeu- cran stable version
install.packages(TimeSeries.OBeu)
# or
# alternatively install the development version from github
devtools::install_github("okgreece/TimeSeries.OBeu")
```
Load library `TimeSeries.OBeu` <img src="obeu_logo.png" align="right" />
```{r}
library(TimeSeries.OBeu)
```
#Time Series analysis in a call
`ts.analysis` is used to estimate *autocorrelation and partial autocorrelation* of input time series data, *autocorrelation and partial autocorrelation* of the model residuals, *trend*, *seasonal* (if exists) and *remainder* components, model parameters such as arima order, arima coefficients etc. and the desired *forecasts* with their corresponding confidence intervals.
`ts.analysis` returns by default a json object, if `tojson` parameter is `FALSE` returns a list object and the default forecast step is set to 1.
```{r}
results = ts.analysis(Athens_executed_ts, prediction.steps = 2, tojson=TRUE) # json string format
jsonlite::prettify(results) # use prettify of jsonlite library to add indentation to the returned JSON string
```
`ts.analysis` uses internally the functions `ts.stationary.test`,`ts.acf`,`ts.non.seas.decomp`,`ts.seasonal.decomp`, `ts.seasonal.model`, `ts.non.seas.model` and `ts.forecast`. However, these functions can be used independently and depends on the user requirements (see package manual or vignettes).
#Time series analysis on OpenBudgets.eu platform
`open_spending.ts` is designed to estimate and return the autocorrelation parameters, time series model parameters and the forecast parameters of [OpenBudgets.eu](http://openbudgets.eu/) time series datasets.
The input data must be a JSON link according to the [OpenBudgets.eu data model](https://github.com/openbudgets/data-model). The user should specify the amount and time variables, future steps to be predicted (default is 1 step forward) and the arima order (if not specified the most appropriate model will be selected according to AIC value).
`open_spending.ts` estimates and returns the json data (that are described with the [OpenBudgets.eu data model](https://github.com/openbudgets/data-model)), using `ts.analysis` function.
```{r}
#example openbudgets.eu time series data
sample.ts.data =
'{"page":0,
"page_size": 30,
"total_cell_count": 15,
"cell": [],
"status": "ok",
"cells": [{
"global__fiscalPeriod__28951.notation": "2002",
"global__amount__0397f.sum": 290501420.64,
"global__amount__0397f__CZK.sum": 9210928544.2325,
"_count": 4805
},
{
"global__fiscalPeriod__28951.notation": "2003",
"global__amount__0397f.sum": 311242291.07,
"global__amount__0397f__CZK.sum": 9832143974.9013,
"_count": 4988
},
{
"global__fiscalPeriod__28951.notation": "2004",
"global__amount__0397f.sum": 5268500701.1,
"global__amount__0397f__CZK.sum": 170688885714.24,
"_count": 10055
},
{
"global__fiscalPeriod__28951.notation": "2005",
"global__amount__0397f.sum": 2542887761.01,
"global__amount__0397f__CZK.sum": 77204615312.025,
"_count": 2032
},
{
"global__fiscalPeriod__28951.notation": "2006",
"global__amount__0397f.sum": 14803951786.68,
"global__amount__0397f__CZK.sum": 429758720367.32,
"_count": 13632
},
{
"global__fiscalPeriod__28951.notation": "2007",
"global__amount__0397f.sum": 16188514346.44,
"global__amount__0397f__CZK.sum": 445588857385.76,
"_count": 22798
},
{
"global__fiscalPeriod__28951.notation": "2008",
"global__amount__0397f.sum": 18231035815.89,
"global__amount__0397f__CZK.sum": 480643028250.12,
"_count": 24176
},
{
"global__fiscalPeriod__28951.notation": "2009",
"global__amount__0397f.sum": 19079541164.68,
"global__amount__0397f__CZK.sum": 511808691742.54,
"_count": 26250
},
{
"global__fiscalPeriod__28951.notation": "2010",
"global__amount__0397f.sum": 22738650575.01,
"global__amount__0397f__CZK.sum": 597685430364.14,
"_count": 87667
},
{
"global__fiscalPeriod__28951.notation": "2011",
"global__amount__0397f.sum": 24961375670.57,
"global__amount__0397f__CZK.sum": 626230992823.26,
"_count": 134352
},
{
"global__fiscalPeriod__28951.notation": "2012",
"global__amount__0397f.sum": 261513607691.41,
"global__amount__0397f__CZK.sum": 7030666436872.5,
"_count": 147556
},
{
"global__fiscalPeriod__28951.notation": "2013",
"global__amount__0397f.sum": 268946402299.09,
"global__amount__0397f__CZK.sum": 7226220232913.8,
"_count": 150079
},
{
"global__fiscalPeriod__28951.notation": "2014",
"global__amount__0397f.sum": 255222816704.9,
"global__amount__0397f__CZK.sum": 6907598086283.4,
"_count": 176019
},
{
"global__fiscalPeriod__28951.notation": "2015",
"global__amount__0397f.sum": 22976062973.62,
"global__amount__0397f__CZK.sum": 636276111928.46,
"_count": 213777
},
{
"global__fiscalPeriod__28951.notation": "2016",
"global__amount__0397f.sum": 12051686541.16,
"global__amount__0397f__CZK.sum": 325672725401.77,
"_count": 161797
}
],
"order": [
["global__fiscalPeriod__28951.fiscalPeriod", "asc"]
],
"aggregates": ["", "_count"],
"summary": {
"global__amount__0397f.sum": 945126777743.27,
"global__amount__0397f__CZK.sum": 25485085887878
},
"attributes": [""]
}'
result = open_spending.ts(
json_data = sample.ts.data,
time ="global__fiscalPeriod__28951.notation",
amount = "global__amount__0397f.sum"
)
# Pretty output using prettify of jsonlite library
jsonlite::prettify(result,indent = 2)
```