## 1 Introduction  
Analysis of monitoring data from very slowly moving landslides faces very difficult issues considering the precision and accuracy of the used monitoring instruments, the influence of the different environmental factors on the measured data, the frequency of the measurements and the overall complexity of the phenomena [1; 2; 3; 4]. In the case of large landslides (of more than several km\({}^{2}\)) another problem arises with choosing the right placement of the monitoring devices on the ground, as remote sensing methods such as LiDAR or InSAR, despite their enormous applicability, still do not provide comparable sub-mm accuracy [5; 6].  
When analyzing the landslide movement monitoring results, the time-series analysis should be used. Time-series analysis comprises different statistical methods to understand the underlying context of data points or to make forecasts [7]. It can capture seasonal behaviors, trends and changes [8]. Time-series analysis is routinely applied in econometrics [9; 10]. In geosciences, there is a constant use of time-series analysis within GNSS observations [11] or climatology [12]. Thus, it might be a bit surprising that in landslide science, time-series analysis studies are mostly related to remote sensing(SAR) data analysis [13; 14; 15]. To some point, this is not surprising as SAR data are available in defined time-steps and form an undisrupted time series, which is the basic assumption of the analysis. In the case of landslides studies, time-series analysis is usually focused on trend behavior, sudden changes and seasonality analysis [16; 17; 18]. The big advantage of time-series analysis methods lies in the possibility to make predictions of future behavior based on previous activity. This is especially important in landslide disaster management. Complex non-linear time series are ubiquitous in geosciences [19]. The recently developed adaptive regression models (used in this work) appear to offer good solutions in multivariate non-stationary time-series analyses [20].  
In the era of Big Data, it is more and more possible to harvest/gather a large amount of environmental and geophysical data related to slope stability using a variety of sensors and systems with high precision [21; 22], which can be used for interpreting and monitoring the results and finding correlations to understand the landslide activity. However, a recent study [23] highlighted that the observations are prohibitively site-specific, highly subjective and fail to account for the spatial variability and correlations in slope movements and common trigger factors.  
The landslide movement is usually neither smooth nor continuous. There are changes in the nature of the observed monitoring data at certain moments (changes of speed or direction of movement, jump events). We assume that these changes (events) during the development of landslide movements are caused by extreme states of factors (triggers) affecting the landslide movement (i.e., seismicity, precipitation) [24].  
In this paper, we analyzed data from precise 3D dilatometers installed on the detachment planes of a giant complex landslide in a volcanic environment. The time-series analysis was performed on 1D, 2D and 3D data projections using adaptive regression. The main aim of this work was to find a suitable methodology for evaluating the time series of micro-movements to find the changes in landslide movement (events) in them and then compare them with the time moments of extreme external conditions including available climatic and seismic data to find correlations with the possible triggers of the landslide activity. The aim was fulfilled by applying known methods and combining them to understand better the landslide behavior and its responses to possible influencing factors.