Browsing by Subject "time-series analysis"

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  • Shin, Bokyong; Rask, Mikko (2021)
    Online deliberation research has recently developed automated indicators to assess the deliberative quality of much user-generated online data. While most previous studies have developed indicators based on content analysis and network analysis, time-series data and associated methods have been studied less thoroughly. This article contributes to the literature by proposing indicators based on a combination of network analysis and time-series analysis, arguing that it will help monitor how online deliberation evolves. Based on Habermasian deliberative criteria, we develop six throughput indicators and demonstrate their applications in the OmaStadi participatory budgeting project in Helsinki, Finland. The study results show that these indicators consist of intuitive figures and visualizations that will facilitate collective intelligence on ongoing processes and ways to solve problems promptly.
  • Luoma, Ville; Yrttimaa, Tuomas; Kankare, Ville; Saarinen, Ninni; Pyorala, Jiri; Kukko, Antero; Kaartinen, Harri; Hyyppa, Juha; Holopainen, Markus; Vastaranta, Mikko (2021)
    Tree growth is a multidimensional process that is affected by several factors. There is a continuous demand for improved information on tree growth and the ecological traits controlling it. This study aims at providing new approaches to improve ecological understanding of tree growth by the means of terrestrial laser scanning (TLS). Changes in tree stem form and stem volume allocation were investigated during a five-year monitoring period. In total, a selection of attributes from 736 trees from 37 sample plots representing different forest structures were extracted from taper curves derived from two-date TLS point clouds. The results of this study showed the capability of point cloud-based methods in detecting changes in the stem form and volume allocation. In addition, the results showed a significant difference between different forest structures in how relative stem volume and logwood volume increased during the monitoring period. Along with contributing to providing more accurate information for monitoring purposes in general, the findings of this study showed the ability and many possibilities of point cloud-based method to characterize changes in living organisms in particular, which further promote the feasibility of using point clouds as an observation method also in ecological studies.
  • Tang, Zhipeng; Amatulli, Giuseppe; Pellikka, Petri; Heiskanen, Janne (2022)
    The number of Landsat time-series applications has grown substantially because of its approximately 50-year history and relatively high spatial resolution for observing long term changes in the Earth's surface. However, missing observations (i.e., gaps) caused by clouds and cloud shadows, orbit and sensing geometry, and sensor issues have broadly limited the development of Landsat time-series applications. Due to the large area and temporal and spatial irregularity of time-series gaps, it is difficult to find an efficient and highly precise method to fill them. The Missing Observation Prediction based on Spectral-Temporal Metrics (MOPSTM) method has been proposed and delivered good performance in filling large-area gaps of single-date Landsat images. However, it can be less practical for a time series longer than one year due to the lack of mechanics that exclude dissimilar data in time series (e.g., different phenology or changes in land cover). To solve this problem, this study proposes a new gap-filling method, Spectral Temporal Information for Missing Data Reconstruction (STIMDR), and examines its performance in Landsat reflectance time series. Two groups of experiments, including 2000 x 2000 pixel Landsat single-date images and Landsat time series acquired from four sites (Kenya, Finland, Germany, and China), were performed to test the new method. We simulated artificial gaps to evaluate predicted pixel values with real observations. Quantitative and qualitative evaluations of gap-filled images through comparisons with other state-of-the-art methods confirmed the more robust and accurate performance of the proposed method. In addition, the proposed method was also able to fill gaps contaminated by extreme cloud cover for a period (e.g., winter in high-latitude areas). A down-stream task of random forest supervised classification through both gap-filled simulated datasets and the original valid datasets verified that STIMDR-generated products are relevant to the user community for land cover applications.
  • Kanniainen, Vesa; Mellin, Ilkka (HECER, Helsinki Center of Economic Research, 2017)
    HECER Discussion Paper ; 408
    The aggregate economic development of Finland and Sweden defined by the growth of the real Gross Domestic Products (GDPs) advanced in tandem for a long time. The current paper provides a descriptive statistical analysis producing stylized facts of the co-development of the two neighboring countries. Using various statistical techniques, the paper documents that the tandem is over. The paper identifies the break-up point in 2007 when the financial crisis started to culminate peaking in 2008-2009. The key test on the duration of the economic tandem will be provided by the forecast ability of the statistical vector autoregressive model to be identified and estimated for GDPs of the two countries. The stability of the model is used as a statistical criterion. A rich set of results on the comparative volatility and instability, the steepness of recessions, and the diverging welfare of the two economies are reported. In particular, it is estimated that the cumulative welfare gap between the countries, measured by the cumulative prediction error of the model in the post-tandem period 2008/1 - 2015/1 is 47.9 per cent.