Construct Time Series Research Articles

The Use of Photogrammetry to Construct Time Series of Vegetation Permeability to Water and Seed Transport in Agricultural Waterways

Terrestrial vegetation has numerous positive effects on the main regulating services of agricultural channels, such as seed retention, pollutant mitigation, bank stabilization, and sedimentation, and this vegetation acts as a porous medium for the flow of matter through the channels. This vegetation also limits the water conveyance in channels, and consequently is frequently removed by farmers to increase its porosity. However, the temporal effects of these management practices remain poorly understood. Indeed, the vegetation porosity exhibits important temporal variations according to the maintenance schedule, and the water level also varies with time inside a given channel section according to rainfall events or irrigation practices. To maximise the impacts of vegetation on agricultural channels, it is now of primary importance to measure vegetation porosity according to water level over a long time period rather than at a particular time. Time series of such complex vegetation characteristics have never been studied using remote sensing methods. Here, we present a new approach using the Structure-from-Motion approach using a Multi-View Stereo algorithm (SfM-MVS) technique to construct time series of herbaceous vegetation porosity in a real agricultural channel managed by five different practices: control, dredging, mowing, burning, and chemical weeding. We post-processed the time series of point clouds to create an indicator of vegetation porosity for the whole section and of the surface of the channel. Mowing and chemical weeding are the practices presenting the most favorable temporal evolutions of the porosity indicators regarding flow events. Burning did not succeed in restoring the porosity of the channel due to quick recovery of the vegetation and dephasing of the maintenance calendar with the flow events. The high robustness of the technique and the automatization of the SfM-MVS calculation together with the post-processing of the point clouds should help in handling time series of SfM-MVS data for applications in ecohydrology or agroecology.

Measuring Technological Innovation over the Long Run

We use textual analysis of high-dimensional data from patent documents to create new indicators of technological innovation. We identify important patents based on textual similarity of a given patent to previous and subsequent work: these patents are distinct from previous work but are related to subsequent innovations. Our importance indicators correlate with existing measures of patent quality but also provide complementary information. We identify breakthrough innovations as the most important patents—those in the right tail of our measure—and construct time-series indices of technological change at the aggregate and sectoral level. Our technology indices capture the evolution of technological waves over a long time span (1840 to the present) and cover innovation by private and public firms, as well as non-profit organizations and the US government. Advances in electricity and transportation drive the index in the 1880s; chemicals and electricity in the 1920s and 1930s; and computers and communication in the post-1980s.

Using ships of opportunity for array element localization and relative channel impulse response estimation

The uncertainty of estimating relative channel impulse responses (CIRs) obtained using the radiated signature from a ship of opportunity is investigated. The ship observations were taken during a 1.4 km (11 min) transect during the Noise Correlation 2009 (NC09) experiment. Beamforming on the angle associated with the direct ray-path yields an estimate of the ship signature, subsequently used as a matched filter. Relative CIRs are estimated every 2.5 s independently at three vertical line arrays (VLAs) for a total of 270 observations per VLA. The estimated relative arrival-time uncertainty is inversely proportional to source bandwidth and CIR signal-to-noise ratio, and reached a minimum standard deviation of 5 μs (approximately 1 cm). The direct-path relative arrival-times are used to construct time series for each VLA element across the 11 min observation interval. The overall structure of these time series compares favorably with that predicted from an array element localization (AEL) model that exhibits sensitivity on the order of centimeters. The short-term standard deviations calculated on direct-path (7 μs) and bottom-reflected-path (17 μs) time series are in agreement with the estimated arrival-time accuracies. The implication of these observed arrival-time accuracies in the context of making sound speed perturbation and bottom-depth estimates is discussed.The uncertainty of estimating relative channel impulse responses (CIRs) obtained using the radiated signature from a ship of opportunity is investigated. The ship observations were taken during a 1.4 km (11 min) transect during the Noise Correlation 2009 (NC09) experiment. Beamforming on the angle associated with the direct ray-path yields an estimate of the ship signature, subsequently used as a matched filter. Relative CIRs are estimated every 2.5 s independently at three vertical line arrays (VLAs) for a total of 270 observations per VLA. The estimated relative arrival-time uncertainty is inversely proportional to source bandwidth and CIR signal-to-noise ratio, and reached a minimum standard deviation of 5 μs (approximately 1 cm). The direct-path relative arrival-times are used to construct time series for each VLA element across the 11 min observation interval. The overall structure of these time series compares favorably with that predicted from an array element localization (AEL) model that exhibits...

Mapping Damage-Affected Areas after Natural Hazard Events Using Sentinel-1 Coherence Time Series

The emergence of the Sentinel-1A and 1B satellites now offers freely available and widely accessible Synthetic Aperture Radar (SAR) data. Near-global coverage and rapid repeat time (6–12 days) gives Sentinel-1 data the potential to be widely used for monitoring the Earth’s surface. Subtle land-cover and land surface changes can affect the phase and amplitude of the C-band SAR signal, and thus the coherence between two images collected before and after such changes. Analysis of SAR coherence therefore serves as a rapidly deployable and powerful tool to track both seasonal changes and rapid surface disturbances following natural disasters. An advantage of using Sentinel-1 C-band radar data is the ability to easily construct time series of coherence for a region of interest at low cost. In this paper, we propose a new method for Potentially Affected Area (PAA) detection following a natural hazard event. Based on the coherence time series, the proposed method (1) determines the natural variability of coherence within each pixel in the region of interest, accounting for factors such as seasonality and the inherent noise of variable surfaces; and (2) compares pixel-by-pixel syn-event coherence to temporal coherence distributions to determine where statistically significant coherence loss has occurred. The user can determine to what degree the syn-event coherence value (e.g., 1st, 5th percentile of pre-event distribution) constitutes a PAA, and integrate pertinent regional data, such as population density, to rank and prioritise PAAs. We apply the method to two case studies, Sarpol-e, Iran following the 2017 Iran-Iraq earthquake, and a landslide-prone region of NW Argentina, to demonstrate how rapid identification and interpretation of potentially affected areas can be performed shortly following a natural hazard event.

An active fire detection algorithm based on multi-temporal FengYun-3C VIRR data

The Visible and Infra-Red Radiometer (VIRR) is an improved third-generation sensor used for Earth observation with channels ranging from visible to thermal bands and carried on board the Chinese FengYun-3C satellite. An active fire detection algorithm based on VIRR data has already been designed and tested in different study areas worldwide. Most of the previously related algorithms were developed merely focusing on the spatial and spectral features of pixels while the temporal attributes of these observed active fires were ignored. In this research, multi-temporal VIRR data were used to construct time series of pixels. The core content of the algorithm consists of the changes in the time-series profiles together with the observed data. By calculating the predicted mid-infrared (MIR) value and the stable MIR value of the target area, fire pixels can be easily distinguished. To assess the performance of this algorithm, a total of eight target areas distributed across the world were used for testing. Two stages of validation were carried out with data of different spatial resolutions. A rough comparison was carried out first. During this step, results from Collection 6 of MODIS Fire and Thermal Anomalies products (MOD14A1) and results generated from the previously used algorithm were used for comparison. The detailed validation work was conducted with the support of Landsat series (including ETM+ and OLI sensors) data even though the different imaging time may affect the actual validation results.

Some considerations on China’s long-run economic growth: 1952–2015 from the analysis of factor contributions to that of the profit rate

This article offers methodological reflexions on China’s long-term economic growth. We first construct time series of physical capital stocks going from 1952, close to the date of formation of the People’s Republic, until 2015, by taking into account the latest yearbooks (I). Then, we test this new database to estimate the contributions of the production factors to GDP growth within the framework of various neoclassical models, highlighting the limitations of the latter (II). After that, an original framework is mobilized, in the spirit of the recent researches provided by Thomas Piketty, who combines mainstream references with components borrowing from Keynesian as well as neoinstitutionalist formalisations. Here, several problems associated with such researches are identified (III). Finally, we move the discussion towards a more promising approach, involving profit rate indicators, to deepen future studies of China’s long-run economic growth (IV).

Mold breakout prediction in slab continuous casting based on combined method of GA-BP neural network and logic rules

Given advantages of artificial intelligent technology, the genetic algorithm (GA) and back propagation (BP) neural network is used to construct time series model for recognizing temperature change waveform of single thermocouple in mold breakout process. Based on breakout mechanism, the logic rules are used to construct spatial model of multi-thermocouples for identifying two-dimensional (2D) propagation behavior of the sticker. Time series model based on GA-BP neural network and spatial model based on logic rules form a new breakout prediction method. And the simulation and field test of this method are carried out for validating its performance. Simulation results of time series model show that the GA-BP neural network has better recognition precision than BP neural network for sticking temperature pattern of single thermocouple. And simulation results of spatial model show that it can predict all stickers accurately and timely, with no missed alarm and false alarm. Furthermore, field test results show that this breakout prediction method has detection ratio of 100% and a lower false alarm frequency (0.1365% times/heat), which is better than actual breakout prediction system used in continuous casting production. So the combined method of GA-BP neural network and logic rules is feasible and effective in breakout prediction and can be used in more intelligently industrial process.

Daily extreme precipitation and trends over China

Based on daily precipitation data of more than 2000 Chinese stations and more than 50 yr, we constructed time series of extreme precipitation based on six different indices for each station: annual and summer maximum (top-1) precipitation, accumulated amount of 10 precipitation maxima (annual, summer; top-10), and total annual and summer precipitation. Furthermore, we constructed the time series of the total number of stations based on the total number of stations with top-1 and top-10 annual extreme precipitation for the whole data period, the whole country, and six subregions, respectively. Analysis of these time series indicate three regions with distinct trends of extreme precipitation: (1) a positive trend region in Southeast China, (2) a positive trend region in Northwest China, and (3) a negative trend region in North China. Increasing (decreasing) ratios of 10–30% or even >30% were observed in these three regions. The national total number of stations with top-1 and top-10 precipitation extremes increased respectively by 2.4 and 15 stations per decade on average but with great inter-annual variations. There have been three periods with highly frequent precipitation extremes since 1960: (1) early 1960s, (2) middle and late 1990s, and (3) early 21st century. There are significant regional differences in trends of regional total number of stations with top-1 and top-10 precipitation. The most significant increase was observed over Northwest China. During the same period, there are significant changes in the atmospheric variables that favor the decrease of extreme precipitation over North China: an increase in the geopotential height over North China and its upstream regions, a decrease in the low-level meridional wind from South China coast to North China, and the corresponding low moisture content in North China. The extreme precipitation values with a 50-year empirical return period are 400–600 mm at the South China coastal regions and gradually decrease to less than 50 mm in Northwest China. The mean increase rate in comparison with 20-year empirical return levels is 6.8%. The historical maximum precipitation is more than twice the 50-year return levels.

Study on Secure Dynamic Covering Algorithm for E-Logistics Information in a Cloud Computing Platform

For traditional coverage method, local adaptive weighted search is adopted for subspace reconstruction of e-logistics information, which requires significant iterative computations and leads to large coverage errors and unsatisfactory results. This paper proposes a secure dynamic covering algorithm for e-logistics information based on the basis of directional clustering for the envelope of optimization solution vectors. A data network distribution model of e-logistics information on a cloud computing platform is constructed to extract features of e-logistics information and to construct time series of information flows. The directional clustering algorithm for the envelope of optimization solution vectors is introduced to schedule features of e-logistics information. The experimental results show that the proposed algorithm has higher coverage rate, smaller error, and increases performance of e-logistics information transmission and higher application value on a cloud computing platform.

Millionaires or Job Creators: What Really Happens to Employment Growth When You Stick It to the Rich?

We provide empirical evidence on the consequences of relatively higher tax burdens on the rich for aggregate employment growth using a newly constructed time series for 1947 through 2011 derived from the US Statistics of Income. In response to shifts in the relative federal tax burden toward the rich, we find statistically significant positive effects on employment growth in the short run and some evidence of negative effects on employment growth in the long run. Among our robustness checks, we use the Romer and Romer narrative record analysis to restrict our sample to a period of exclusively exogenous tax changes. The results hold in the restricted sample and are also consistent across alternative specifications and estimation methods, including unrestricted and Bayesian vector autoregressive.

Absolute water storages in the Congo River floodplains from integration of InSAR and satellite radar altimetry

Floodplains delay the transport of water, dissolved matter and sediments by storing water during flood peak seasons. Estimation of water storage over the floodplains is essential to understand the water balances in the fluvial systems and the role of floodplains in nutrient and sediment transport. However, spatio-temporal variations of water storages over floodplains are not well known due to their remoteness, vastness, and high temporal variability. In this study, we propose a new method to estimate absolute water storages over the floodplains by establishing relations between water depths (d) and water volumes (V) using 2-D water depth maps from the integration of Interferometric Synthetic Aperture Radar (InSAR) and altimetry measurements. We applied this method over the Congo River floodplains and modeled the d−V relation using a power function (note that d−V indicates relation between d and V, not d minus V), which revealed the cross-section geometry of the floodplains as a convex curve. Then, we combined this d−V relation and Envisat altimetry measurements to construct time series of floodplain's absolute water storages from 2002 to 2011. Its mean annual amplitude over the floodplains (~7,777km2) is 3.86±0.59km3 with peaks in December, which lags behind total water storage (TWS) changes from the Gravity Recovery and Climate Experiment (GRACE) and precipitation changes from Tropical Rainfall Measuring Mission (TRMM) by about one month. The results also exhibit inter-annual variability, with maximum water volume to be 5.9±0.72km3 in the wet year of 2002 and minimum volume to be 2.01±0.63km3 in the dry year of 2005. The inter-annual variation of water storages can be explained by the changes of precipitation from TRMM.

How Have Banks Been Managing the Composition of High-Quality Liquid Assets?

We study banks' post-crisis liquidity management. We construct time series of U.S. banks’ holdings of high-quality liquid assets (HQLA) and examine how these assets have been managed in recent years to comply with the Liquidity Coverage Ratio (LCR) requirement. We find that, in becoming LCR compliant, banks initially ramped up their stock of reserve balances. However, once the requirement was met, some banks subsequently shifted the compositions of their liquid portfolios significantly. This raises the question: What drives the compositions of banks’ HQLA? We show that a risk-return framework can account for a range of potential portfolio compositions depending on banks’ tolerance for interest rate risk. And, our data indicate that banks have indeed adopted a range of portfolio compositions, with some components exhibiting a high degree of daily variance. These findings lead us to conclude that about half of large banks are largely focused on risk-return conside rations in managing the compositions of their HQLA pools while the other half appear bound by other factors. We highlight the importance of our findings for both the transmission and implementation of monetary policy.

Measuring uncertainty based on rounding: New method and application to inflation expectations

The literature on cognition and communication documents that people use round numbers to convey uncertainty. This paper introduces a method of quantifying the uncertainty associated with round responses in pre-existing survey data. I construct micro-level and time series measures of inflation uncertainty since 1978. Inflation uncertainty is countercyclical and correlated with inflation disagreement, volatility, and the Economic Policy Uncertainty index. Inflation uncertainty is lowest among high-income consumers, college graduates, males, and stock market investors. More uncertain consumers are more reluctant to spend on durables, cars, and homes. Round responses are common on many surveys, suggesting numerous applications of this method.

An Argo mixed layer climatology and database

AbstractA global climatology and database of mixed layer properties are computed from nearly 1,250,000 Argo profiles. The climatology is calculated with both a hybrid algorithm for detecting the mixed layer depth (MLD) and a standard threshold method. The climatology provides accurate information about the depth, properties, extent, and seasonal patterns of global mixed layers. The individual profile results in the database can be used to construct time series of mixed layer properties in specific regions of interest. The climatology and database are available online at http://mixedlayer.ucsd.edu. The MLDs calculated by the hybrid algorithm are shallower and generally more accurate than those of the threshold method, particularly in regions of deep winter mixed layers; the new climatology differs the most from existing mixed layer climatologies in these regions. Examples are presented from the Labrador and Irminger Seas, the Southern Ocean, and the North Atlantic Ocean near the Gulf Stream. In these regions the threshold method tends to overestimate winter MLDs by approximately 10% compared to the algorithm.

Issue Highlights

Issue Highlights

The Moscow Market in Country's Economic Space

This paper studies integration of the Moscow market for final goods with markets of all other Russian regions. It considers an aggregated market represented by a minimum food basket. The law of one price serves as a criterion of market integration. It is a base for constructing time series models of the regional costs of the staples basket over 2001–2015 relative to its cost in Moscow. Regional markets are divided into four groups: integrated with the Moscow market, conditionally integrated with it, not integrated but tending towards integration with the Moscow market, and neither integrated nor tending towards integration. Nonlinear time series models with asymptotically decaying trends describe the movement towards integration (price convergence).

A stable Faroe Bank Channel overflow 1995–2015

Abstract. The Faroe Bank Channel (FBC) is the deepest passage across the Greenland–Scotland Ridge (GSR) and there is a continuous deep flow of cold and dense water passing through it from the Arctic Mediterranean into the North Atlantic and further to the rest of the world ocean. This FBC overflow is part of the Atlantic Meridional Overturning Circulation (AMOC), which has recently been suggested to have weakened. From November 1995 to May 2015, the FBC overflow has been monitored by a continuous ADCP (acoustic Doppler current profiler) mooring, which has been deployed in the middle of this narrow channel. Combined with regular hydrography cruises and several short-term mooring experiments, this allowed us to construct time series of volume transport and to follow changes in the hydrographic properties and density of the FBC overflow. The mean kinematic overflow, derived solely from the velocity field, was found to be (2.2 ± 0.2) Sv (1 Sv = 106 m3 s−1) with a slight, but not statistically significant, positive trend. The coldest part, and probably the bulk, of the FBC overflow warmed by a bit more than 0.1 °C, especially after 2002, increasing the transport of heat into the deep ocean. This warming was, however, accompanied by increasing salinities, which seem to have compensated for the temperature-induced density decrease. Thus, the FBC overflow has remained stable in volume transport as well as density during the 2 decades from 1995 to 2015. After crossing the GSR, the overflow is modified by mixing and entrainment, but the associated change in volume (and heat) transport is still not well known. Whatever effect this has on the AMOC and the global energy balance, our observed stability of the FBC overflow is consistent with reported observations from the other main overflow branch, the Denmark Strait overflow, and the three Atlantic inflow branches to the Arctic Mediterranean that feed the overflows. If the AMOC has weakened during the last 2 decades, it is not likely to have been due to its northernmost extension – the exchanges across the Greenland–Scotland Ridge.

The observational signature of modelled torsional waves and comparison to geomagnetic jerks

Torsional Alfvén waves involve the interaction of zonal fluid flow and the ambient magnetic field in the core. Consequently, they perturb the background magnetic field and induce a secondary magnetic field. Using a steady background magnetic field from observationally constrained field models and azimuthal velocities from torsional wave forward models, we solve an induction equation for the wave-induced secular variation (SV). We construct time series and maps of wave-induced SV and investigate how previously identified propagation characteristics manifest in the magnetic signals, and whether our modelled travelling torsional waves are capable of producing signals that resemble jerks in terms of amplitude and timescale. Fast torsional waves with amplitudes and timescales consistent with a recent study of the 6yr ΔLOD signal induce very rapid, small (maximum ∼2nT/yr at Earth’s surface) SV signals that would likely be difficult to be resolve in observations of Earth’s SV. Slow torsional waves with amplitudes and timescales consistent with other studies produce larger SV signals that reach amplitudes of ∼20nT/yr at Earth’s surface. We applied a two-part linear regression jerk detection method to the SV induced by slow torsional waves, using the same parameters as used on real SV, which identified several synthetic jerk events. As the local magnetic field morphology dictates which regions are sensitive to zonal core flow, and not all regions are sensitive at the same time, the modelled waves generally produce synthetic jerks that are observed on regional scales and occur in a single SV component. However, high wave amplitudes during reflection from the stress-free CMB induce large-scale SV signals in all components, which results in a global contemporaneous jerk event such as that observed in 1969. In general, the identified events are periodic due to waves passing beneath locations at fixed intervals and the SV signals are smoothly varying. These smooth signals are more consistent with the geomagnetic jerks envisaged by Demetrescu and Dobrica than the sharp ‘V’ shapes that are typically associated with geomagnetic jerks.

National accounting for infrastructure

This article discusses how more systematic information can be compiled about the state of the UK infrastructure. It stresses the importance of considering the stock and not just the flow of new infrastructure investment. It draws attention to the information about the balance sheet that is contained for all sectors of the economy in the National Accounts, as part of their integrated stock-flow structure. While there appears to be no internationally agreed convention about the definition of infrastructure, the National Accounts information can be used to construct time series estimates for the infrastructure on a common sense view of its coverage. In constant price terms, the ratio of the infrastructure stock to gross value added appears to have risen since the mid-1990s, back towards the levels of the 1970s. But that is not true in current price terms, where the ratio remains well below the levels of the 1970s. The explanation seems to be that a relative price effect has occurred: infrastructure has become cheaper in relative terms but that has not resulted in increased infrastructure intensity. The article notes other information about the state of the UK infrastructure. As they mature, the Whole of Government Accounts should be a rich source of information. So, too, might the UK Capital Services index. Ultimately, all this information could feed into a more systematic growth accounting framework for the UK.

Adaptive Order Determination for Constructing Time Series Forecasting Models

In time series modeling consistent criteria like Bayesian Information Criterion (BIC) outperform in terms of predictability loss-efficient criteria like Akaike Information Criterion (AIC) when data are generated by a finite-order autoregressive process, and the reverse is true when data are generated by an infinite-order autoregressive process. Since in practice we don’t know the data-generating process, it is useful to have an adaptive criterion that behaves as either a consistent or just as a loss-efficient criterion, whichever performs better. Here we derive such a criterion. Moreover, our criterion is adaptive to effective sample sizes and not sensitive to maximum a priori determined order limits.