Representing Earth remote sensing data as time series
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.
This article discusses questions of price forecast for innovative product. Time series have been used in order to predict price movements. For this propose the price (for 24 months) of innovative product, Samsung Galaxy Nexus I9250, was chosen. Based on this information prices for the product were calculated after six months and a year. Also, using results of this forecast the model for prediction the price of innovative product was developed.
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 scientic collaborations and exchanges among attendees, and therefore, ITISE 2017 solicits high-quality original research papers (including signicant 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.
For the first time spatio-temporal characteristics of air pollution by sulfur dioxide (SO2), nitrogen dioxide (NO2), carbon dioxide (CO2), carbon monoxide (CO) and aerosol over Ukraine and Europe are established. It was shown that moderate risks of air pollution by sulfur dioxide of eastern and western parts of Ukraine relate as 2:1. It was shown that values of moderate and high risks of European areas most polluted by aerosol (except the north of Italy) and Ukraine (Kyiv, Donetsk and Odessa regions) are approximately related as 1:1. Moderate levels of risks for Kiev, Donetsk and Odessa regions relate to moderate risk levels of other Ukrainian regions as 1.8:1. The maximum risk value of moderate pollution by nitrogen dioxide of the atmosphere of Europe and Ukraine relate as 3:1. The analysis of concentration dynamics of carbon dioxide for atmosphere of the whole earth for the last 8 years (2004–2011) revealed the increase for more than 20 ppm. It is shown that the atmosphere of Ukraine exposed to the same level of carbon monoxide pollution, as the atmosphere of other European countries.
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.
The analysis of short-term tendency of economic dynamics can be performed on seasonally adjusted data only. This implies that each time series is to be transformed in two: the seasonal component and the remaining part. The result of such decomposition depends on the specific features of the seasonal adjustment algorithm. Most uncertainty is expected within the neighborhood of crises when the economic indicators are likely to demonstrate substantial changes. Under such circumstances, the seasonal adjustment procedures are likely to generate spurious signals that deteriorate the seasonally adjusted series.
In this paper we analyze distortions of seasonally adjusted time series of economic data that appear in the neighborhood of crises. We examined the aberrations caused by sharp level shifts as well as by changes in seasonal pattern and showed that under these circumstances the standard algorithms of seasonal adjustment can generate spurious signals similar to first signs of a crisis or its second and following waves. We consider these misleading signals from two points of view: first, as an economic historian who operates with long time series of unchanging data; second, as an analyst of short-term dynamics monitoring the data that is subject to revisions.
We show that these aberrations can be misleading for understanding of short-run dynamics especially during the first years after a crisis. The identification of the end of a recession and estimation of seasonally adjusted values of observations right after the peak (or bottom) of a fluctuation seem to be the most problematic. Monitoring within this “blind zone” appears to be very complicated. We compared aberrations produced by X-12-ARIMA and TRAMO/SEATS. Some recommendations to soften the distortions are proposed.
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.
For the first time, using satellite Earth remote sensing data, the maps of air pollution risks by nitrogen dioxide (NO2) over the territory of Europe with spatial resolution of 0.25º×0.25º (approximately 27.5 km × 18 km for the 48º latitude) were created. The suggested risk calculation technique is simple yet delivers extensive understanding of typical air pollution character. It is shown that the highest risks of air pollution by nitrogen dioxide in Europe are observed over Germany, Belgium, Netherlands and southern part of the North Sea as well as over large cities.