Continuous GPS Time Series Data Analysis in Sumatera; Case of Study: Continuous Data SuGAR (Sumatran GPS Array) 2004-2007 - Sarah Leila Hanief; Irwan Meilano; Dudy Darmawan Wijaya (Indonesian Journal of Geospatial, 2012)
Abstract. In each GPS time series data, there are signals which exist and affect the result that has been received, which is called noise. Those noise components will form certain pattern in time series. Basically, time series has periodic component which commonly not being able to be detected directly. To detect which periodic component that dominantly affect the time series, there is a way which is called spectral analysis. With acknowledge periodic component in a time series, we can know the characteristic of the time series and then we can determine how many parameters will be needed to do curve fitting. There are two approximations in fitting, it is either linear fitting only or linear fitting with including periodic component. As a comparison between these two methods, we need to be estimate displacements velocity rate in a year. From the analysis that has been done, the result is that the biggest difference of displacements velocity rate between these two methods is 3.7 milimeters per year.Keywords: displacements velocity rate, fitting, periodic component, spectral analysis, time series.