Machine learning methods for modelling and analysis of time series signals in geoinformatics
In this dissertation is provided a comparative analysis that evaluates the performance of several deep learning (DL) architectures on a large number of time series datasets of different nature and for different applications. Two main fruitful research fields are discussed here which were strategically chosen in order to address current cross disciplinary research priorities attracting the interest of geodetic community. The first problem is related to ionospheric Total Electron Content (TEC) modeling which is an important issue in many real time Global Navigation System Satellites (GNSS) applications. Reliable and fast knowledge about ionospheric variations becomes increasingly important. GNSS users of single frequency receivers and satellite navigation systems need accurate corrections to remove signal degradation effects caused by the ionosphere. Ionospheric modeling using signal processing techniques is the subject of discussion in the present contribution. The next problem under discussion is energy disaggregation which is an important issue for energy efficiency and energy consumption awareness. Reliable and fast knowledge about residential energy consumption at appliance level becomes increasingly important nowadays and it is an important mitigation measure to prevent energy wastage. Energy disaggregation or Nonintrusive load monitoring (NILM) is a single channel blind source separation problem where the task is to estimate the consumption of each electrical appliance given the total energy consumption. For both problems various deep learning models (DL) are proposed that cover various aspects of the problem under study, whereas experimental results indicate the proposed methods superiority compared to the current state of the art.
Code (0)
등록된 구현이 없습니다.
Tasks
BIG-bench Machine Learningblind source separationTime SeriesTime Series AnalysisSimilar Papers 제목 키워드 기반
On the balance between the training time and interpretability of neural ODE for time series modelling
Most machine learning methods are used as a black box for modelling. We may try to extract some knowledge from physics-based training methods, such as neural ODE (ordinary differential equation). Neural ODE has advantage…
Time SeriesTime Series AnalysisA Review of Open Source Software Tools for Time Series Analysis
Time series data is used in a wide range of real world applications. In a variety of domains , detailed analysis of time series data (via Forecasting and Anomaly Detection) leads to a better understanding of how events a…
Anomaly DetectionTime SeriesTime Series AnalysisFor2For: Learning to forecast from forecasts
This paper presents a time series forecasting framework which combines standard forecasting methods and a machine learning model. The inputs to the machine learning model are not lagged values or regular time series feat…
BIG-bench Machine LearningTime SeriesTime Series AnalysisTime Series ForecastingScalable Machine Learning Algorithms using Path Signatures
The interface between stochastic analysis and machine learning is a rapidly evolving field, with path signatures - iterated integrals that provide faithful, hierarchical representations of paths - offering a principled a…
Computational EfficiencyGaussian ProcessesTime SeriesTime Series ForecastingRecent advances in data-driven methods for degradation modelling across applications
Understanding degradation is crucial for ensuring the longevity and performance of materials, systems, and organisms. To illustrate the similarities across applications, this article provides a review of data-based metho…
Time Series