57 research outputs found
Interest rates mapping
The present study deals with the analysis and mapping of Swiss franc interest
rates. Interest rates depend on time and maturity, defining term structure of
the interest rate curves (IRC). In the present study IRC are considered in a
two-dimensional feature space - time and maturity. Geostatistical models and
machine learning algorithms (multilayer perceptron and Support Vector Machines)
were applied to produce interest rate maps. IR maps can be used for the
visualisation and patterns perception purposes, to develop and to explore
economical hypotheses, to produce dynamic asses-liability simulations and for
the financial risk assessments. The feasibility of an application of interest
rates mapping approach for the IRC forecasting is considered as well.Comment: 8 pages, 8 figures. Presented at Applications of Physics in Financial
Analysis conference (APFA6), Lisbon, Portugal, 200
Data-driven topo-climatic mapping with machine learning methods
Automatic environmental monitoring networks enforced by wireless communication technologies provide large and ever increasing volumes of data nowadays. The use of this information in natural hazard research is an important issue. Particularly useful for risk assessment and decision making are the spatial maps of hazard-related parameters produced from point observations and available auxiliary information. The purpose of this article is to present and explore the appropriate tools to process large amounts of available data and produce predictions at fine spatial scales. These are the algorithms of machine learning, which are aimed at non-parametric robust modelling of non-linear dependencies from empirical data. The computational efficiency of the data-driven methods allows producing the prediction maps in real time which makes them superior to physical models for the operational use in risk assessment and mitigation. Particularly, this situation encounters in spatial prediction of climatic variables (topo-climatic mapping). In complex topographies of the mountainous regions, the meteorological processes are highly influenced by the relief. The article shows how these relations, possibly regionalized and non-linear, can be modelled from data using the information from digital elevation models. The particular illustration of the developed methodology concerns the mapping of temperatures (including the situations of Föhn and temperature inversion) given the measurements taken from the Swiss meteorological monitoring network. The range of the methods used in the study includes data-driven feature selection, support vector algorithms and artificial neural network
Circadian patterns of Wikipedia editorial activity: A demographic analysis
Wikipedia (WP) as a collaborative, dynamical system of humans is an
appropriate subject of social studies. Each single action of the members of
this society, i.e. editors, is well recorded and accessible. Using the
cumulative data of 34 Wikipedias in different languages, we try to characterize
and find the universalities and differences in temporal activity patterns of
editors. Based on this data, we estimate the geographical distribution of
editors for each WP in the globe. Furthermore we also clarify the differences
among different groups of WPs, which originate in the variance of cultural and
social features of the communities of editors
Monitoring network optimisation for spatial data classification using support vector machines
The paper presents a novel method for monitoring network optimisation,
based on a recent machine learning technique known as support vector
machine. It is problem-oriented in the sense that it directly answers
the question of whether the advised spatial location is important
for the classification model. The method can be used to increase
the accuracy of classification models by taking a small number of
additional measurements. Traditionally, network optimisation is performed
by means of the analysis of the kriging variances. The comparison
of the method with the traditional approach is presented on a real
case study with climate data
Applying machine learning methods to avalanche forecasting
Avalanche forecasting is a complex process involving the assimilation of multiple data sources to make predictions over varying spatial and temporal resolutions. Numerically assisted forecasting often uses nearest neighbour methods (NN), which are known to have limitations when dealing with high dimensional data. We apply Support Vector Machines to a dataset from Lochaber, Scotland to assess their applicability in avalanche forecasting. Support Vector Machines (SVMs) belong to a family of theoretically based techniques from machine learning and are designed to deal with high dimensional data. Initial experiments showed that SVMs gave results which were comparable with NN for categorical and probabilistic forecasts. Experiments utilising the ability of SVMs to deal with high dimensionality in producing a spatial forecast show promise, but require further work
Kernel-based mapping of orographic rainfall enhancement in the Swiss Alps as detected by weatherradar
In this paper, we develop a data-driven methodology to characterize
the likelihood of orographic precipitation enhancement using sequences
of weather radar images and a digital elevation model (DEM). Geographical
locations with topographic characteristics favorable to enforce repeatable
and persistent orographic precipitation such as stationary cells,
upslope rainfall enhancement, and repeated convective initiation
are detected by analyzing the spatial distribution of a set of precipitation
cells extracted from radar imagery. Topographic features such as
terrain convexity and gradients computed from the DEM at multiple
spatial scales as well as velocity fields estimated from sequences
of weather radar images are used as explanatory factors to describe
the occurrence of localized precipitation enhancement. The latter
is represented as a binary process by defining a threshold on the
number of cell occurrences at particular locations. Both two-class
and one-class support vector machine classifiers are tested to separate
the presumed orographic cells from the nonorographic ones in the
space of contributing topographic and flow features. Site-based validation
is carried out to estimate realistic generalization skills of the
obtained spatial prediction models. Due to the high class separability,
the decision function of the classifiers can be interpreted as a
likelihood or susceptibility of orographic precipitation enhancement.
The developed approach can serve as a basis for refining radar-based
quantitative precipitation estimates and short-term forecasts or
for generating stochastic precipitation ensembles conditioned on
the local topography
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