Vadim Timonin

Learn More
The paper presents some contemporary approaches to the spatial environmental data analysis, processing and presentation. The main topics are concentrated on the decision–oriented problems of environmental and pollution spatial data mining and modelling: valorisation and representativity of data with the help of exploratory data analysis, topological,(More)
Nowadays machine learning (ML), including Artificial Neural Networks (ANN) of different architectures and Support Vector Machines (SVM), provides extremely important tools for intelligent geo-and environmental data analysis, processing and visualisation. Machine learning is an important complement to the traditional techniques like geostatistics. This paper(More)
Direct Neural Network Residual Kriging (DNNRK) is a two step algorithm (Kanevsky et al. 1995). The first step includes estimating large scale structures by using artificial neural networks (ANN) with simple sum of squares error function. ANN, being universal approximators, model overall non-linear spatial pattern fairly well. ANN are model free estimators(More)
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(More)
The present research deals with the review of the analysis and modeling of Swiss franc interest rate curves (IRC) by using unsupervised (SOM, Gaussian Mixtures) and supervised machine (MLP) learning algorithms. IRC are considered as objects embedded into different feature spaces: maturities; maturity-date, parameters of Nelson-Siegel model (NSM). Analysis(More)
The present study deals with the empirical analysis of patterns in the evolution of interest rate curves (IRC). The main topic is to consider IRC as objects (curves) embedded into high-dimensional space and to study similarities and differences between them. This is a typical problem of clustering and classification in machine learning. In fact, theses data(More)
897,.9 The work is devoted to an application of artificial neural network (multilayer perceptron) and conditional stochastic simulations to electricity load forecasting in Russia. One of the problems is missing data and some important weather parameters (wind, cloudiness, precipitation, historical information). This gives rise to rather large forecasting(More)
Recently, kernel-based Machine Learning methods have gained great popularity in many data analysis and data mining fields: pattern recognition, biocomputing, speech and vision, engineering, remote sensing etc. The paper describes the use of kernel methods to approach the processing of datasets from environmental monitoring networks. Several typical problems(More)