Article
Что сохранит для истории современная российская статистика?
Problems of transformation of modern Russian statistics in future historical statistics about our time are considered. Existence of serious obstacles on the way of this transformation is noted, their reasons are analyzed. Requirements to that part of historical statistics about our time which data are used for the analysis of economic dynamics are discussed. Measures for the situation improvement are offered, estimated consequences of their implementation or failure are analyzed.
In this research we compare the performance of different data mining techniques in the analysis of electroencephalogram (EEG) data. We study the question od predicting post-comatose neuro-developmental scores based mainly on statistical features of the EEG recordings. We compare results from applying different data mining techniques, such as the Elastic Net, Lasso, Gaussian Support Vector Regression and Random Forest Regression. We also compare the results produced with different matrix completion methods.
We are proud to present the set of final accepted papers for the fourth edition of the ITISE 2017 conference "International work-conference on Time Series" held in Granada (Spain) during September, 18-20, 2017. The ITISE 2017 (International work-conference on Time Series) seeks to provide a discussion forum for scientists, engineers, educators and students about the latest ideas and realizations in the foundations, theory, models and applications for interdisciplinary and multidisciplinary re- search encompassing disciplines of computer science, mathematics, statistics, forecaster, econometric, etc, in the field of time series analysis and forecasting. The aims of ITISE 2017 is to create a friendly environment that could lead to the establish- ment or strengthening of scientific collaborations and exchanges among attendees, and therefore, ITISE 2017 solicits high-quality original research papers (including significant work-in-progress) on any aspect time series analysis and forecasting, in order to motivating the generation, and use of knowledge and new computational techniques and methods on forecasting in a wide range of fields.
We are proud to present the set of nal accepted papers for the fourth edition of the ITISE 2017 conference "International work-conference on Time Series" held in Granada (Spain) during September, 18-20, 2017. The ITISE 2017 (International work-conference on Time Series) seeks to provide a discussion forum for scientists, engineers, educators and students about the latest ideas and realizations in the foundations, theory, models and applications for interdisciplinary and multidisciplinary research encompassing disciplines of computer science, mathematics, statistics, forecaster, econometric, etc, in the eld of time series analysis and forecasting. The aims of ITISE 2017 is to create a friendly environment that could lead to the establishment or strengthening of scienti c collaborations and exchanges among attendees, and therefore, ITISE 2017 solicits high-quality original research papers (including signi cant work-in-progress) on any aspect time series analysis and forecasting, in order to motivating the generation, and use of knowledge and new computational techniques and methods on forecasting in a wide range of elds.
To date, all remote sensing data are represented and stored as temporal sequences of separate “snapshots” – rasters or grids. This makes impossible to quickly obtain a time series of a variable values for the full available period for a region of a coordinate grid. Trend research – one of the most important topics in Earth science – becomes extremely complex and time consuming. This paper proposes an alternative data representation and corresponding storage technique. The data are represented as a collection of individual time series, one per each grid cell or raster pixel. New storage layout enables any time series to be always readily accessible. This approach considerably facilitates the application of existing time series techniques to remote sensing, climate reanalysis and similar data as well as provides new research and development opportunities not available before.
The paper analyzes storage peculiarities of satellite Earth remote sensing data time series. We propose methods for their compression based on the discovered peculiarities exploiting different schemes of Huffman coding. One of the proposed methods reaches 6% increase in the compression ratio (93%) in contrast to the deflate method used in Java SE6 (87%), for a time series of aerosol optical thickness derived from MODIS radiometer of TERRA satellite. Further improvement can be achieved by using the entropy coding of floating point numbers.
We consider certain spaces of functions on the circle, which naturally appear in harmonic analysis, and superposition operators on these spaces. We study the following question: which functions have the property that each their superposition with a homeomorphism of the circle belongs to a given space? We also study the multidimensional case.
We consider the spaces of functions on the m-dimensional torus, whose Fourier transform is p -summable. We obtain estimates for the norms of the exponential functions deformed by a C1 -smooth phase. The results generalize to the multidimensional case the one-dimensional results obtained by the author earlier in “Quantitative estimates in the Beurling—Helson theorem”, Sbornik: Mathematics, 201:12 (2010), 1811 – 1836.
We consider the spaces of function on the circle whose Fourier transform is p-summable. We obtain estimates for the norms of exponential functions deformed by a C1 -smooth phase.