ts.analysis: Time series analysis results for OBEU Time series

Description Usage Arguments Details Value Author(s) See Also Examples

View source: R/ts.analysis.R

Description

Univariate time series analysis for short and long time series data using the appropriate model.

Usage

1
ts.analysis(tsdata, x.order=NULL, prediction.steps=1, tojson=T)

Arguments

tsdata

The input univariate time series data

x.order

An integer vector of length 3 specifying the order of the Arima model

prediction.steps

The number of prediction steps

tojson

If TRUE the results are returned in json format, default returns a list

Details

This function automatically tests for stationarity of the input time series data using ts.stationary.test function. Depending the nature of the time series data and the stationary tests there are four branches: a.)short and non seasonal, b.)short and seasonal, c.)long and non seasonal and d.)long and seasonal. For branches a and c ts.non.seas.model is used and for b and d ts.seasonal.model is used.

This function also decomposes both seasonal and non seasonal time series through ts.non.seas.decomp and ts.seasonal.decomp and forecasts h steps ahead the user selected(default h=1) using ts.forecast.

Value

A json string with the parameters:

Author(s)

Kleanthis Koupidis, Charalampos Bratsas

See Also

ts.stationary.test, ts.acf, ts.seasonal.model, ts.seasonal.decomp, ts.non.seas.model, ts.non.seas.decomp, ts.forecast

Examples

1
ts.analysis(Athens_draft_ts, prediction.steps=3)

okgreece/TimeSeries.OBeu documentation built on June 17, 2018, 7:51 a.m.