Coherent Time Series Research Articles

The FAOSTAT database of greenhouse gas emissions from agriculture

Greenhouse gas (GHG) emissions from agriculture, including crop and livestock production, forestry and associated land use changes, are responsible for a significant fraction of anthropogenic emissions, up to 30% according to the Intergovernmental Panel on Climate Change (IPCC). Yet while emissions from fossil fuels are updated yearly and by multiple sources—including national-level statistics from the International Energy Agency (IEA)—no comparable efforts for reporting global statistics for agriculture, forestry and other land use (AFOLU) emissions exist: the latest complete assessment was the 2007 IPCC report, based on 2005 emission data. This gap is critical for several reasons. First, potentially large climate funding could be linked in coming decades to more precise estimates of emissions and mitigation potentials. For many developing countries, and especially the least developed ones, this requires improved assessments of AFOLU emissions. Second, growth in global emissions from fossil fuels has outpaced that from AFOLU during every decade of the period 1961–2010, so the relative contribution of the latter to total climate forcing has diminished over time, with a need for regular updates. We present results from a new GHG database developed at FAO, providing a complete and coherent time series of emission statistics over a reference period 1961–2010, at country level, based on FAOSTAT activity data and IPCC Tier 1 methodology. We discuss results at global and regional level, focusing on trends in the agriculture sector and net deforestation. Our results complement those available from the IPCC, extending trend analysis to a longer historical period and, critically, beyond 2005 to more recent years. In particular, from 2000 to 2010, we find that agricultural emissions increased by 1.1% annually, reaching 4.6 Gt CO2 yr−1 in 2010 (up to 5.4–5.8 Gt CO2 yr−1 with emissions from biomass burning and organic soils included). Over the same decade 2000–2010, the ratio of agriculture to fossil fuel emissions has decreased, from 17.2% to 13.7%, and the decrease is even greater for the ratio of net deforestation to fossil fuel emissions: from 19.1% to 10.1%. In fact, in the year 2000, emissions from agriculture have been consistently larger—about 1.2 Gt CO2 yr−1 in 2010—than those from net deforestation.

ANALISIS FAKTOR-FAKTOR YANG MEMPENGARUHI PERMINTAAN UANG DI INDONESIA TAHUN 1999 : Q1 - 2010 : Q4 DENGAN PENDEKATAN ERROR CORECTION MODELS (ECM)

Money has a very important role in the modern economy. Money demand function is often used by central banks in various state as a tool to determine the target growth of money or the money supply. Make up money supply to encourage excessive price increases that exceed the level expected in the long run can undermine economic growth. Conversely, if the increase in the money supply to low the debility economic will happen. This study aims to analyze the factors that influence the demand for money in Indonesia in the short and long run. This research used data coherent time or time series period of 1999:Q1 to 2010 : Q4. The data used are quarterly. Model used the tool of error correction model of econometrical analysis. This model can explain the behavior of short and long run. The results showed (1) the inflation variable in the short and long run has positive and significant impact on the demand for money in Indonesia. (2) the interest rate variable in the short run has no significant negative impact and long run has negative and significant impact on the demand for money in Indonesia. (3) the GDP variable in short run is not significant and long run has positive and significant impact on the demand for money in Indonesia. (4) Error Corection Term marked positive and significant indicate valid models and can used to estimate the demand for money in Indonesia. Based on the results of this study concluded that significant inflation variable in influencing the demand for money in Indonesia, both in the short and long run. The interest rate and GDP variable in short run is not significant, while in long run both are significant impact on demand for money in Indonesia. Bank Indonesia as the monetary authority in Indonesia expect maintain monetary stability through a guard against inflation, as the research results in both the short and long run inflation significant influence the demand for money in Indonesia.

FAKTOR-FAKTOR YANG MEMPENGARUHI IMPORBERAS DI INDONESIA TAHUN 1980-2009

Indonesia merupakan salah satu negara produsen beras terbesar di dunia. Sebagai negara penghasil beras Indonesia masih mengimpor beras untuk memenuhi konsumsi beras dalam negeri. Hal ini tidak sesuai dengan data yang menunjukkan bahwa produksi beras dalam negeri mengalami surplus. Dengan produksi beras yang surplus seharusnya pemerintah dapat memenuhi kebutuhan beras dalam negeri tanpa perlu mengimpor beras. Penelitian ini bertujuan untuk mengetahui pengaruh variabel independen (produksi beras, jumlah penduduk dan produk domestik bruto) terhadap variabel dependen (impor beras) baik dalam jangka pendek maupun jangka panjang. Penelitian ini menggunakan data runtun waktu atau time series. Model analisis yang digunakan adalah alat analisis ekonometrika model koreksi kesalahan (Error Correction Model/ECM) dan asumsi klasik. Model ini dapat menjelaskan perilaku jangka pendek maupun jangka panjang. Hasil penelitian menunjukkan (1) variabel produksi dalam jangka pendek maupun jangka panjang berpengaruh negatif dan signifikan terhadap impor beras Indonesia. (2) variabel penduduk dalam jangka pendek maupun jangka panjang tidak ada pengaruh terhadap impor beras Indonesia. (3) Variabel produk domestik bruto dalam jangka pendek tidak ada pengaruh dengan impor beras sedangkan dalam jangka panjang produk domestik bruto berpengaruh positif dan signifikan terhadap impor beras Indonesia. Berdasarkan hasil penelitian dapat disimpulkan bahwa variabel produksi beras dalam jangka pendek berpengaruh signifikan terhadap impor beras sedangkan dalam jangka panjang variabel produksi dan produk domestik bruto berpengaruh signifikan terhadap impor beras. Bagi pemerintah seharusnya memproteksi produk beras dalam negeri supaya pasar tidak dibanjiri oleh produk beras impor, misalkan dengan lebih memaksimalkan penyerapan beras dari para petani lokal, sehingga pasar bisa didominasi oleh produk beras lokal selain itu pemerintah dan petani bekerja sama untuk merevitalisasi bahan pangan agar konsumsi nasional tidak bergantung pada satu bahan pangan saja yaitu beras,

Reconstructing historical trends of Berre lagoon contamination from surface sediment datasets: Influences of industrial regulations and anthropogenic silt inputs

These last decades, the Berre lagoon (in southeastern France) has been deeply affected since the 1930s by strong inputs of contaminants associated with industrial development and since 1966 by huge inputs of freshwater and silts due to the installation of a hydroelectric power plant. Surveys of the surface sediment contamination have been sparsely performed since 1964 for management and research purposes. These surveys were performed by various laboratories that investigated different chemicals and sampling areas using different analysis protocols. Therefore, the available data are disconnected in time and space and differ in quality. In order to reconstruct coherent time series of sediment contamination from this heterogeneous datasets and to discuss the influences of industrial and hydroelectric discharges we used a statistical approach. This approach is based on Principal Component Analysis (PCA) and Fuzzy clustering analysis on data from one extensive survey realized on surface sediments in 1976. The PCA allowed identifying two geochemical indexes describing the main surface sediment geochemical characteristics. The fuzzy clustering analysis on these indexes allowed identifying sub-areas under the specific influence of industrial or hydroelectric discharges. This allowed us to reconstruct, for each sub-area, a coherent and interpretable long-term time series of sediment contamination from the available database. Reconstructed temporal trends allowed us to estimate: (i) the overall decrease of sediment contamination since the mid-1970 attributed to industrial discharge regulations enacted at this period and (ii) the dilution of the concentrations of sediment bound contaminants induced by the hydroelectric power plant and its associated particulate matter inputs.

Constructing a social accounting matrix for Libya

The lack of coherent time series data for Libya is a serious obstacle for applied research on its economy. An alternative to econometric analysis is to use a social accounting matrix (SAM) in conjunction with an appropriately specified computable general equilibrium (CGE) model to address policy questions. We constructed a SAM for Libya for the year 2000, which integrates national income, input-output, flow-of-funds and foreign trade statistics into a comprehensive and consistent matrix. First, a macro-SAM was constructed to capture the macroeconomic framework of the economy. The macro-SAM was then disaggregated into a micro-SAM incorporating the accounts for activities, factors of production and main economic institutions (households, government and the rest of the world). Finally, the micro-SAM was balanced using a cross-entropy procedure in a general algebraic modelling system (GAMS). This matrix facilitates the numerical calibration of large analytical models to provide a suitable vehicle for policy experiments.

Dynamical Aspects of Macroscopic and Quantum Transitions due to Coherence Function and Time Series Events

This study presents the application of dynamical equations able to generate alternating deformations with increasing amplitude and delayed pulses in a certain material medium. It is considered that an external force acts at certain time interval (similar to a time series) upon the material medium in the same area. Using a specific differential equation (considering nonzero initial values and using a function similar to the coherence function between the external force and the deformations inside the material), it results that modulated amplitude oscillations appear inside the material. If the order of the differential dynamical equation is higher, supplementary aspects as different delayed pulses and multiscale behaviour can be noticed. These features are similar to non-Markov aspects of quantum transitions, and for this reason the mathematical model is suitable for describing both quantum phenomena and macroscopic aspects generated by sequence of pulses. An example of a quantum system, namely, the Hydrogen atom, is discussed.

Comparisons between two wavelet functions in extracting coherent structures from solar wind time series

Nowadays, wavelet analysis of turbulent flows have become increasingly popular. However, the study of geometric characteristics from wavelet functions is still poorly explored. In this work we compare the performance of two wavelet functions in extracting the coherent structures from solar wind velocity time series. The data series are from years 1996 to 2002 (except 1998 and 1999). The wavelet algorithm decomposes the annual time-series in two components: the coherent part and non-coherent one, using the daubechies-4 and haar wavelet function. The threshold assumed is based on a percentage of maximum variance found in each dyadic scale. After the extracting procedure, we applied the power spectral density on the original time series and coherent time series to obtain spectral indices. The results from spectral indices show higher values for the coherent part obtained by daubechies-4 than those obtained by the haar wavelet function. Using the kurtosis statistical parameter, on coherent and non-coherent time series, it was possible to conjecture that the differences found between two wavelet functions may be associated with their geometric forms.

Time Evolution of Deformation Using Time Series of Differential Interferograms: Application to La Palma Island (Canary Islands)

Differential interferometry is a very powerful tool for detecting changes in the Earth’s crust where coherence conditions are good, but is difficult to employ in some volcanic areas due to dense vegetation. We apply two differential InSAR methods using the time series associated with the interferograms to perform a phase analysis on a data set for La Palma island (Canary Islands) from the ERS-1 and ERS-2 European Space Agency (ESA) satellites for the time period 1992 to 2000. Both methods involve choosing a master image from the database and creating a series of interferograms with respect to this image. The “Coherent Pixel Time Series” (CPTS) technique chooses pixels with good average coherence, aligns the unwrapped interferograms with a stable area and then performs an inversion to calculate the linear velocity to quantify the deformation. The Coherent Target Modeling (CTM) method calculates the temporal coherence of each pixel to identify stable targets and then determines the best velocity for each pixel by using a linear fit that maximizes the temporal coherence. Using these two methods we have been able to detect deformation on La Palma Island that has been previously undetectable by conventional InSAR methods. There is a roughly circular region on the Southern part of the island that is actively deforming at ∽ -4 to -8 mm/yr. This region is located near the Teneguia valcano, the host of the last known eruption on La Palma in 1971. A thorough investigation of the possible sources for this deformation revealed that it was most likely created by a subsurface thermal source.

Dynamic Spatial Modelling of Regional Convergence Process

Econometric analysis of convergence processes across countries or regions usually refers to a transition period between an arbitrary chosen starting year and a fictitious steady state. Panel unit root tests and panel cointegration techniques have proved to belong to powerful econometric tools if the conditions are met. When referring to economically defined regions, though, it is rather an exception than the rule that coherent time series are available. For this case we introduce a dynamic spatial modelling approach which is suitable to trace regional adjustment processes in space instead of time. It is shown how the spatial error-correction mechanism (SEC model) can be estimated depending on the spatial stationarity properties of the variables under investigation. The dynamic spatial modelling approach presented in this paper is applied to the issue of conditional income and productivity convergence across labour market regions in unified Germany.

Dynamic spatial modelling of regional convergence processes

Econometric analysis of convergence processes across countries or regions usually refers to a transition period between an arbitrary chosen starting year and a fictitious steady state. Panel unit root tests and panel cointegration techniques have proved to belong to powerful econometric tools if the conditions are met. When referring to economically defined regions, though, it is rather an exception than the rule that coherent time series are available. For this case we introduce a dynamic spatial modelling approach which is suitable to trace regional adjustment processes in space instead of time. It is shown how the spatial error-correction mechanism (SEC model) can be estimated depending on the spatial stationarity properties of the variables under investigation. The dynamic spatial modelling approach presented in this paper is applied to the issue of conditional income and productivity convergence across labour market regions in unified Germany.

On the relation between total irradiance and radius variations

We use Singular Spectrum Analysis (SSA) to analyze total solar irradiance variations and CERGA radius measurements. Total solar irradiance has been monitored from space for more than two decades, whilst ground-based radius measurements are available as a coherent time series from 1975. We compare these indicators to try to understand the origin of energy production inside the Sun. One of the main objectives was to assess the reality of the observed variations of the Sun's radius by distinguishing the signal from the noise. Two approaches were used: one using SSA on ground-based data averaged over 90 days, in order to smooth the signal (especially over periods when no data were obtained, mainly in winter time); the second repeats the analysis on individual measurements corrected by reporting data to the zenith. As expected, the level of noise is higher in the first case and the reconstructed noise level, which is large, indicates the difficulty in ascertaining the solar origin in the apparent variability of the solar radius. It is shown from the reconstructed components that the main variation in amplitude (over 930 days) is pronounced during the first part of the measurements and seems to disappear after 1988. There is also a variation with a periodicity of 1380 days, of lower amplitude than that of the shorter component. In both cases, these variations disappear during the rising portion of cycle 23. The first reconstructed component shows that total irradiance varies in parallel with the solar cycle, being higher during maximum activity conditions. The reconstructed radius trend indicates that the solar radius was higher during the minimum of solar cycle 21, but its decrease with the rising activity of cycle 23 is less obvious. The observed value of the solar radius increased by about 0.11 arcsec from the maximum of cycle 21 to the minimum between cycles 21 and 22. Most importantly, we report a long-term radius variation which increased from the maximum of cycle 21 to minimum by about 0.015% , while a smaller decrease (around 0.01% ) is seen from the minimum of cycle 21 to the maximum of cycle 22. This study indicates need for measurements of the degree of the radius changes taken from space, together with total irradiance measurements to establish the phase relation between these two quantities.

Time domain buffeting response calculations of slender structures

For dynamic response predictions of slender civil engineering structures due to wind buffeting forces a time domain calculation procedure has been established by linking simulated wind velocity time series to a quasi-static loading model and a finite element program which is capable of handling non-linear material properties. The coherent time series are obtained from representative cross-spectra by fast Fourier transforms and Monte Carlo simulation. Time domain linear solutions have been compared to equivalent frequency domain results, and good agreement has been obtained. Some parametric studies have been performed with the aim of finding suitable guidelines in the choice of element discretisation, and an example of dynamic response in the presence of non-linear material properties has been included.

Validation of AVHRR Pathfinder SST's over the Mediterranean Sea

Pathfinder Sea Surface Temperatures (SST) have been validated for the Mediterranean Sea using independent data obtained from MEDATLAS CTD and XBT casts. The Mean Bias is, in most of the case less than 0.5° C, with a mean value of 0.2 °C. The bias was uncorrelated to the number of Mediterranean matchups points used to evaluate the coefficients of the SST Pathfinder algorithm. This implies that Pathfinder data over the Mediterranean Sea represents a coherent time series of temperature that can be used for climatological applications. As an example of such an application the time series of the mean Mediterranean SST is discussed for the entire period of available data (1985–1996). The analysis shows evidence of interannual variability but does not suggest an evident increasing trend for SST as suggested by previous works. Comparison with corresponding meteorological data (COADS) confirms the close correlation between meteorological forcings and SST field.

Building a Coherent Time Series with Behavioral Data from Staggered Chambers

Sampling of population with the design of staggered chambers is quite convenient, since it allows data collection from distinct time points at the same clock hour. In this short communication using an ethological approach we report results obtained with behavioral data collected from eight staggered chambers. The eight LD cycles (12:12; L = 350 lux; D = 1 lux, red light) were set with a lag of 3 h, thus completing 24 h. Male Wistar rats 6-month old were individually observed for five minutes in random order within each session along ten consecutive days. Behavioral circadian rhythms of general activity were detected both in the 10-day long time series and in each one of the days, with a statistical significance of 95%. Our results show close similarities with results of individual days, thus pointing to the convenience of such a design.

Temporal fluctuations in coherence of brain waves.

As a measure of dynamical structure, short-term fluctuations of coherence between 0.3 and 100 Hz in the electroencephalogram (EEG) of humans were studied from recordings made by chronic subdural macroelectrodes 5-10 mm apart, on temporal, frontal, and parietal lobes, and from intracranial probes deep in the temporal lobe, including the hippocampus, during sleep, alert, and seizure states. The time series of coherence between adjacent sites calculated every second or less often varies widely in stability over time; sometimes it is stable for half a minute or more. Within 2-min samples, coherence commonly fluctuates by a factor up to 2-3, in all bands, within the time scale of seconds to tens of seconds. The power spectrum of the time series of these fluctuations is broad, extending to 0.02 Hz or slower, and is weighted toward the slower frequencies; little power is faster than 0.5 Hz. Some records show conspicuous swings with a preferred duration of 5-15s, either irregularly or quasirhythmically with a broad peak around 0.1 Hz. Periodicity is not statistically significant in most records. In our sampling, we have not found a consistent difference between lobes of the brain, subdural and depth electrodes, or sleeping and waking states. Seizures generally raise the mean coherence in all frequencies and may reduce the fluctuations by a ceiling effect. The coherence time series of different bands is positively correlated (0.45 overall); significant nonindependence extends for at least two octaves. Coherence fluctuations are quite local; the time series of adjacent electrodes is correlated with that of the nearest neighbor pairs (10 mm) to a coefficient averaging approximately 0.4, falling to approximately 0.2 for neighbors-but-one (20 mm) and to < 0.1 for neighbors-but-two (30 mm). The evidence indicates fine structure in time and space, a dynamic and local determination of this measure of cooperativity. Widely separated frequencies tending to fluctuate together exclude independent oscillators as the general or usual basis of the EEG, although a few rhythms are well known under special conditions. Broad-band events may be the more usual generators. Loci only a few millimeters apart can fluctuate widely in seconds, either in parallel or independently. Scalp EEG coherence cannot be predicted from subdural or deep recordings, or vice versa, and intracortical microelectrodes show still greater coherence fluctuation in space and time. Widely used computations of chaos and dimensionality made upon data from scalp or even subdural or depth electrodes, even when reproducible in successive samples, cannot be considered representative of the brain or the given structure or brain state but only of the scale or view (receptive field) of the electrodes used. Relevant to the evolution of more complex brains, which is an outstanding fact of animal evolution, we believe that measures of cooperativity are likely to be among the dynamic features by which major evolutionary grades of brains differ.

Computer-assisted long-term measurements of urinary output and other biological data

A computerized system is described, combining automatic collection of urine in short intervals (minutes) over long periods (days) and recordings of body temperature, MABP, and heart rate in chronically instrumented conscious dogs. During the studies the dogs are housed in metabolic cages. Indwelling catheters and electrical wires are connected to a specially designed swivel and directed out of the cage to the next room. Infusions, blood sampling, and monitoring can be performed from this room without disturbance to the dogs. Three examples of recordings are given. In one of these examples the sodium excretion patterns on 5 consecutive days under continuous saline infusion in one dog is evaluated. Urine was collected every 20 min. Sodium excretion showed cyclic variations. Fourier analysis exhibited 18-h periods and 4- to 8-h periods. The described system renders, e.g., coherent time series analysis possible for a variety of simultaneously recorded physiological variables and may thus acquire considerable importance for integrative physiology.

Interannual landsat-MSS reflectance variation in an urbanized temperate zone

A chronological time series of 40 Landsat-MSS images over southeastern Canada for the period of 1972–1984 has been analyzed to monitor short-term (seasonal) and long-term variations in land-surface parameters which are either of anthropogenic origin or due to climate-related variability. The data base, generated on the Canadian Landsat Production System, was systematically processed for radiometric correction and atmospheric correction based on clear lakes as standard reflectors and accounting for the background contamination effect. The reflectance values of selected gravel pit sites, which we demonstrate to be stable reflectors, are analyzed as a function of time. The significant and regular variations observed permit the determination of correction coefficients for inter-satellite calibration between the sensors of Landsats 1–5. The resulting coherent time series of corrected reflectances shows a mean seasonal variation for the vegetation phenology (between May and October) which can be modeled by a regression function of the form ( t - t 0)sin[( t - t 0)2 π/ T] for the two near-IR MSS bands. The ground reflectances of the four bands are summed over and combined to compute the long-term “MSS albedo” and normalized vegetation index (NVI) variations. These parameters are analyzed with regard to meteorological variations. The results show that, for the Montreal study area, the yearly mean albedo underwent a significant decreasing trend (− 0.05) since 1972 due to processes of urbanization, deforestation, and decreasing farmland. The effects of these respective land-surface alterations are discussed. For the whole data base, the year-to-year NVI fluctuations correlate with the cumulative amount of summer precipitation which in turn is related to the mean summer evapotranspiration and global solar radiation.

Discrimination of coherent features in turbulent boundary layers by the entropy method

Entropy in information theory is defined as the expected or mean value of the measure of the amount of self-information contained in the ith point of a distribution series x sub i, based on its probability of occurrence p(x sub i). If p(x sub i) is the probability of the ith state of the system in probability space, then the entropy, E(X) = - sigma p(x sub i) logp (x sub i), is a measure of the disorder in the system. Based on this concept, a method was devised which sought to minimize the entropy in a time series in order to construct the signature of the most coherent motions. The constrained minimization was performed using a Lagrange multiplier approach which resulted in the solution of a simultaneous set of non-linear coupled equations to obtain the coherent time series. The application of the method to space-time data taken by a rake of sensors in the near-wall region of a turbulent boundary layer was presented. The results yielded coherent velocity motions made up of locally decelerated or accelerated fluid having a streamwise scale of approximately 100 nu/u(tau), which is in qualitative agreement with the results from other less objective discrimination methods.

A model for numerical simulation of nonstationary sonar reverberation using linear spectral prediction

An innovative approach to the numerical generation of nonstationery reverberation time series is presented and demonstrated. The computer simulated reverberation time series are of high quality, in that they are accurate representations of those which would result from an actual sonar system (transmit/receive and horizontal/ vertical beampatterns; pulse type, shape, length, and power; frequency and sampling rate), platform (speed and depth), and environment (wind speed and direction, backscattering strengths, and propagation loss). Volume, surface, and/or bottom reverberation as seen by a multiple beam sonar on a moving platform is generated. The approach utilizes recent developments in linear spectral prediction research in which the spectra of stochastic processes are modeled as rational functions and algorithms are used to efficiently compute optimal estimates of coefficients which specify the spectra. A two-fold sequence is formulated; first, the expected reverberation spectra for all beams are predicted and, second, the stochastic time series are generated from the expected spectra. The expected spectra are predicted using a numerical implementation, referred to as the REVSPEC (reverberation spectrum) model, of a general formulation of Faure, Ol'shevskii, and Middleton. Given the spectra, the Levinson-Durbin method is used to solve the Yule-Walker equations of the autoregressive formulation of linear spectral prediction. The numerical implementation of the approach, referred to as the REVSIM (reverberation simulation) model, produces nonstationary coherent multiple-beam reverberation time series. The formulation of the REVSIM model is presented and typical results given. A comparison is made between the simulation outputs of the REVSIM model and those of the REVGEN (reverberation generator) model, a standard well-accepted time series simulation model, to demonstrate the validity of the new approach.

Preliminary measurements of the time-lagged coherence of vertical temperature profiles

The coherence between vertical temperature profiles separated in time is studied. Time lags from 12 min to 19 hours and vertical wavelengths from 2 to 300 m are considered. The wavelengths shorter than 10 m show no significant coherence even for 12-min separations. The longer wavelength bands exhibit coherence time series with significant structure out to the largest lag measured. For the wavelength band between about 21 and 42 m the results are directly interpretable as vertical modes. In the other wavelength bands considered, there is evidence that the perturbations are propagating. The coherence and phase time series are compared with the Garrett and Munk internal wave model.