Volume Time Series Research Articles

Monitoring of cherry flowering phenology with Google Trends.

Google Trends (GT) is an online tool designed for searching for changes over time. We assessed its use for evaluating changes in the timing of cherry flowering phenology, which is of intense interest to Japanese people. We examined the relationship between time-series of relative search volume (RSV: relative change in search requests over time obtained from the GT access engine) and cherry flowering information published on websites (as ground truth) in relation to three famous ancient cherry trees. The time-series of RSV showed an annual bell-shaped seasonal variability, and the dates of the maximum RSV tended to correspond to the dates of full bloom. Our results suggest that GT allows monitoring of multiple famous cherry flowering sites where we cannot obtain long-term flowering data to evaluate the spatiotemporal variability of cherry flowering phenology.

FORECASTING PASSENGER TRAFFIC BASED ON STRUCTURE IDENTIFICATION OF NON-STATIONARY TIME SERIES

The article proposes to consider the change in the volume of passenger traffic as non-stationary time series which is characterized by the presence of transients moments that lead to a significant change in the overall trend over time. Studies of changes in demand for passenger traffic in suburban service on bus routes of Cherkasy region in 2019–2021 showed that in the period up to 2020, the daily amount of presented demand on weekdays ranged from 4.1 thousand to 6.4 thousand passengers, and on the second half of 2020, the volume of demand decreased due to applying quarantine restrictions in 1.9–2.1 times (up to 2.1 thousand to 4.1 thousand passengers). Based on the analysis of fluctuations in the actual volume of traffic, the structural and logical inter-component relationship of the formation of demand for transportation was identified, which consists of the allocation of the main groups of transport classes forming the volume of passengers. By adapting the methodological provisions of singular spectral analysis, the main stages of the procedure for identifying the elemental structure of the non-stationary time series of changes in the volume of transportation have been formalized. Based on the analysis of the preconditions for the emergence of transients information on demand for passenger traffic allocated structure elements of the time series of the actual volume of passenger traffic that is based on the features and factors of its occurrence. Implementation of the procedure of using the singular spectral method analysis in the study of changes in the volume of passenger traffic is proposed to do by converting a one-dimensional time series into a matrix of elements that determine the trend, harmonic and random components demand. Based on the analysis of the materials of the passenger survey, it was established that the trend component of the change in demand in the Cherkasy – Smila connection contains two types of labor movements: permanent nature of formation and temporary. For the studied combination during 2019–2021, the share of worker transfers with a permanent character increased from 46.5% to 53.1%, with temporary character – decreased by 30.2% to 20.3%, and the harmonic component - increased from 11.5% to 15.3%, and the random component - remained stable within 11.9% to 11.2%. Based on the synthesis of predicted values of time components series for the connection Cherkasy – Smila found that in terms of implementation positive development scenario, there is a tendency to gradual short-term growth in traffic by 3.5%.

Estimating Lake Water Volume With Regression and Machine Learning Methods

The volume of a lake is a crucial component in understanding environmental and hydrologic processes. The State of Minnesota (USA) has tens of thousands of lakes, but only a small fraction has readily available bathymetric information. In this paper we develop and test methods for predicting water volume in the lake-rich region of Central Minnesota. We used three different published regression models for predicting lake volume using available data. The first model utilized lake surface area as the sole independent variable. The second model utilized lake surface area but also included an additional independent variable, the average change in land surface area in a designated buffer area surrounding a lake. The third model also utilized lake surface area but assumed the land surface to be a self-affine surface, thus allowing the surface area-lake volume relationship to be governed by a scale defined by the Hurst coefficient. These models all utilized bathymetric data available for 816 lakes across the region of study. The models explained over 80% of the variation in lake volumes. The sum difference between the total predicted lake volume and known volumes were <2%. We applied these models to predicting lake volumes using available independent variables for over 40,000 lakes within the study region. The total lake volumes for the methods ranged from 1,180,000- and 1,200,000-hectare meters. We also investigated machine learning models for estimating the individual lake volumes and found they achieved comparable and slightly better predictive performance than from the three regression analysis methods. A 15-year time series of satellite data for the study region was used to develop a time series of lake surface areas and those were used, with the first regression model, to calculate individual lake volumes and temporal variation in the total lake volume of the study region. The time series of lake volumes quantified the effect on water volume of a dry period that occurred from 2011 to 2012. These models are important both for estimating lake volume, but also provide critical information for scaling up different ecosystem processes that are sensitive to lake bathymetry.

Support for the hypothesis that growth of eastern Baltic cod is affected by mild hypoxia. A comment on Svedäng et al. (2022)

Abstract Stock biomass and size composition of eastern Baltic cod (EBC) has declined, with low recent stock productivity attributed to loss of major spawning areas, reduced growth rates, and possibly increased natural mortality. Svedäng et al. present valuable new data for evaluating the causes of these changes. However, contrary to their own conclusions, those new data actually provide both direct and indirect support for the hypothesis that the reduced growth rate observed may be due to a decline in ambient oxygen for small cod in the Bornholm Basin area (SD25). Their long time series of reproductive volume (RV) extends our knowledge of historic changes in the limits to spawning imposed by salinity and oxygen. However, the causes and consequences of such spawning area changes are well established. Therefore, this Comment only addresses the contentious debate about causes of decline in growth.

Merging Landsat and airborne LiDAR observations for continuous monitoring of floodplain water extent, depth and volume

The Darling River system in Australia is under pressure from water extraction and climate change. Management interventions such as environmental flow releases require understanding of water storage dynamics and the connectivity of floodplains and wetlands. Such knowledge can be gleaned from the long observational record of the Landsat series of satellite sensors and high (<5 m) resolution digital elevation models derived from airborne light detection and ranging (LiDAR). Here, for the first time, we develop and demonstrate an approach to reconstruct 16-day floodplain water dynamics, including extent, depth, and volume for a long Landsat time series (1987 to present). Time series mapping of surface water extent at 5-m resolution was achieved by topographic downscaling of Landsat-derived surface water data. We propose a simple and effective algorithm to restore missing data in the images caused by, e.g., cloud and shadows, swath edges and the Landsat 7 Scan Line Corrector (SLC) failure, thereby increasing the number of useable images five-fold. The 5-m surface water extent maps clearly delineate the narrow river channel and the boundary of floodplain wetlands. They can capture the development, peak and retreat of flood events. By combining Landsat and airborne LiDAR observations, we produced time series of surface water depth mapping at 5-m resolution, accounting for the degree of hydraulic surface water connectivity. Based on these maps, we derived 16-day floodplain volume dynamics for 1987 to present. The correlation coefficient between upstream river flow records and floodplain volume time series was 0.88, indicating that the estimates were robust. The algorithms developed can be used for ongoing very high-resolution mapping to assist in managing human water use and environmental health in the Murray-Darling Basin.

Population-scale dietary interests during the COVID-19 pandemic

The SARS-CoV-2 virus has altered people’s lives around the world. Here we document population-wide shifts in dietary interests in 18 countries in 2020, as revealed through time series of Google search volumes. We find that during the first wave of the COVID-19 pandemic there was an overall surge in food interest, larger and longer-lasting than the surge during typical end-of-year holidays in Western countries. The shock of decreased mobility manifested as a drastic increase in interest in consuming food at home and a corresponding decrease in consuming food outside of home. The largest (up to threefold) increases occurred for calorie-dense carbohydrate-based foods such as pastries, bakery products, bread, and pies. The observed shifts in dietary interests have the potential to globally affect food consumption and health outcomes. These findings can inform governmental and organizational decisions regarding measures to mitigate the effects of the COVID-19 pandemic on diet and nutrition.

Feedback between surface and deep processes: Insight from time series analysis of sedimentary record

Sediment accumulation and subduction at continental margins are hypothesized to be crucial in the operation of Wilson cycle style plate tectonics on Earth. To test this hypothesis, we explore the interaction between continental sedimentation and plate tectonics by utilizing a 3.5 Gyr continent-scale sediment abundance record from the Macrostrat database. Wavelet analysis of a time series of sedimentary rock volume identified a supercontinent-like cycle of ∼500 Myr across the past 3.0 Gyr. Intriguingly, this long-term fluctuation in net sedimentation record that indicates continent-derived sediment flux into oceanic crust is in phase with the vigor of subduction activity (as characterized by zircon εHf and δ18O isotopes) through time. These agreements provide independent evidence for the hypothesis that the operation of Wilson cycle style plate tectonics was facilitated by sediment accumulation and subduction (lubrication) at continental margins. Moreover, wavelet cross-correlation analysis of sediment abundance and subduction flux suggests an increasingly efficient sediment cycling via subduction since ∼3.0 Ga, notably coinciding with transition periods of atmospheric oxygen levels at the beginning and end of the Proterozoic. The results suggest that the co-evolving surface process and plate tectonics that increased long-term net sequestration of organic carbon could have contributed to the initial rise of atmospheric oxygen at the beginning of the Proterozoic and the climb to sustained modern levels in the early Phanerozoic.

Shift-Invariant-Subspace Discretization and Volume Reconstruction for Light Field Microscopy

Light Field Microscopy (LFM) is an imaging technique that captures 3D spatial information with a single 2D image. LFM is attractive because of its relatively simple implementation and fast volume acquisition rate. Capturing volume time series at a camera frame rate can enable the study of the behaviour of many biological systems. For instance, it could provide insights into the communication dynamics of living 3D neural networks. However, conventional 3D reconstruction algorithms for LFM typically suffer from high computational cost, low lateral resolution, and reconstruction artifacts. In this work, we study the origin of these issues and propose novel techniques to improve the performance of the reconstruction process. First, we propose a discretization approach that uses shift-invariant subspaces to generalize the typical discretization framework used in LFM. Then, we study the shift-invariant-subspace assumption as a prior for volume reconstruction under ideal conditions. Furthermore, we present a method to reduce the computational time of the forward model by using singular value decomposition (SVD). Finally, we propose to use iterative approaches that incorporate additional priors to perform artifact-free 3D reconstruction from real light field images. We experimentally show that our approach performs better than Richardson-Lucy-based strategies in computational time, image quality, and artifact reduction.

Impact of COVID-19 Pandemic on Population-Level Interest in Skincare: Evidence from a Google Trends

COVID-19 pandemic created an unprecedented disruption of daily life including the pattern of skincare in healthcare settings by issuing stay-at-home orders around the world. There has been limited information about trends of skincare-related public interest during CVOID-19 and whether any substantial disruption in population-level behavior. The objective of this study is to evaluate the change in skincare-related population interest around the world during the COVID-19 pandemic time. Weekly RSV data were extracted worldwide and in 25 counties between August 1, 2016, and August 31, 2020. Interrupted time-series analysis was conducted as the quasi-experimental approach to evaluate the longitudinal effects of COVID-19 skincare-related search queries. For each country, autoregressive integrated moving average (ARIMA) model relative search volume (RSV) time series and then testing multiple periods simultaneously to examine the magnitude of the interruption. Multivariate linear regression was used to estimate the correlation between the relative changes in RSV with COVID-19 confirmed cases/ per million population and lockdown measures. Of 25 included countries in our study, 17 showed significantly increased (p&lt;0.01) RSVs during the lockdown period compared with ARIMA forecasted data. The highest percentage of increments occurs in May and June in most countries. There was also a significant correlation between lockdown measures and the number of COVID-19 cases with relative changes in population interests for skincare. Understanding the trend and changes in skincare public interest during COVID-19 may assist health authorities to promote accessible educational information and preventive initiatives regarding skin problems.

Paternal preconception exposure to chronic morphine alters respiratory pattern in response to morphine in male offspring

The clinical use of opioids is restricted by its deleterious impacts on respiratory system. Gaining a better understanding of an individual’s susceptibility to adverse opioid effects is important to recognize patients at risk. Ancestral drug addiction has been shown to be associated with alterations in drug responsiveness in the progenies. In the current study, we sought to evaluate the effects of preconception paternal morphine consumption on respiratory parameters in response to acute morphine in male offspring during adulthood, using plethysmography technique. Male Wistar rats administered 10 days of increasing doses of morphine in the period of adolescence. Thereafter, following a 30-day abstinence time, adult males copulated with naïve females. The adult male offspring were examined for breathing response to morphine.Our results indicated that sires who introduce chronic morphine during adolescence leads to increase irregularity of respiratory pattern and asynchronization between inter-breath interval (IBI) and respiratory volume (RV) time series in male offspring. These findings provide evidence that chronic morphine use by parents even before pregnancy can affect respiratory pattern and response to morphine in the offspring.

Development of Predictive Models for Water Budget Simulations of Closed-Basin Lakes: Case Studies of Lakes Azuei and Enriquillo on the Island of Hispaniola

The historical water level fluctuations of the two neighboring Caribbean lakes of Azuei (LA) and Enriquillo (LE) on Hispaniola have shown random periods of synchronous and asynchronous behaviors, with both lakes exhibiting independent dynamics despite being exposed to the same climatic forces and being directly next to each other. This paper examines their systems’ main drivers and constraints, which are used to develop numerical models for these two lakes. The water balance approach was employed to conceptually model the lakes on an interannual scale and examine the assumptions of surface and subsurface processes. These assumptions were made based on field observations and prior studies. The developed models were optimized and calibrated for 1984 to 2017 and then validated for the period 1972 to 1984 based on the lakes’ observational volume change and volume time series. The models yielded “good” performance, with NSE averaged at 0.7 and RE averaged at 13% for volume change. The performance improved to “very good” for volume simulations, with NSE averaging higher than 0.9 and RE averaging at 1%. The uncertainty analysis showed a p-factor of 0.73 and an r-factor of 1.7 on average, supporting the reliability and precision of the results. Analyzing the time series of the lakes and quantifying the main elements of the water balance, each lake’s shrinkage and expansion phases were explored, and the drivers of such behavior were identified for each lake. The main drivers of LE’s system are North Atlantic cyclone activities and uncontrolled inter-basin water transfer, and direct rainfall and evaporation to/from its surface. For LA, its system is controlled mainly by groundwater fluxes in and out of it, despite possessing small values in its water budget.

A pre-predictive analysis of trading volume in the options market

Subject. This article examines the relationship between the three indicators of the derivatives market, namely price, trading volume, and open interest. Objectives. The article aims to characterize the dynamics of option trading volumes in terms of regularity and predictability of changes. Methods. For the study, we used general scientific methods. Results. The article substantiates the expediency of using fractal analysis methods to identify the stability of market trends, describes approaches to the classification of options, on the basis of which the range of options traded on the Moscow Exchange is characterized, and conducts a pre-predictive analysis of the time series of trading volume. Relevance. The results of the study can be useful to persons studying financial markets, market analysts and developers of option contracts.

Container Volume Prediction Using Time-Series Decomposition with a Long Short-Term Memory Models

The purpose of this study is to improve the prediction of container volumes in Busan ports by applying external variables and time-series data decomposition methods to deep learning prediction models. Previous studies on container volume forecasting were based on traditional statistical methodologies, such as ARIMA, SARIMA, and regression. However, these methods do not explain the complexity and variability of data caused by changes in the external environment, such as the global financial crisis and economic fluctuations. Deep learning can explore the inherent patterns of data and analyze the characteristics (time series, external environmental variables, and outliers); hence, the accuracy of deep learning-based volume prediction models is better than that of traditional models. However, this does not include the study of overall trends (upward, steady, or downward). In this study, a novel deep learning prediction model is proposed that combines prediction and trend identification of container volume. The proposed model explores external variables that are related to container volume, combining port volume time-series decomposition with external variables and deep learning-based multivariate long short-term memory (LSTM) prediction. The results indicate that the proposed model performs better than the traditional LSTM model and follows the trend simultaneously.

The use of traffic data from automatic monitoring systems to obtain day-to-day time series of vehicle traffic volumes and origin-destination flows in urban networks

The understanding of travel pattern dynamics in the urban environment is essential for the transportation systems planning and operation. Recently, the increasing availability of massive traffic data from traffic monitoring systems, including automatic number plate recognition systems (TMS-ANPR), can allow an understanding of the day-to-day variability of traffic flows in large urban network systems. However, to enhance the data quality for analysis, it is essential to carry out a previous data treatment. This work presents a method for treatment of TMS-ANPR data. The main product of this data treatment are the day-to-day time series of traffic volumes and OD flows for different periods of a typical day, allowing the analysis of the multiday dynamic of travel behavior and of the model assumptions stated in the literature about such dynamic behavior. The proposed method, which can be applied to any type of TMS-ANPR, was applied to generate time series data from the TMS-ANPR of Fortaleza city, contributing to identify suspicious and atypical data, to define representative patterns of vehicular traffic and to estimate series of OD flows.

COVID-19, media coverage of bats and related Web searches: a turning point for bat conservation?

SARS‐CoV‐2, the virus that caused the COVID‐19 pandemic, is genomically similar to a SARS‐like beta‐coronavirus found in Asian rhinolophid bats. This evolutionary relationship impressed the global media, which then emphasised bats as key actors in the spillover that resulted in the pandemic. In this study, we highlight changes in the traditional and new media coverage of bats and in Internet search volumes that occurred since the beginning of the COVID‐19 pandemic in 2020.We analysed Google and Wikipedia searches for bats and coronaviruses in 21 countries and eight languages, as well as television broadcasts in the USA, some of which have global coverage, between January 2016 and December 2020. In January 2020, the amount of television news about bats boomed, and news associated with the term ‘bat’ shifted to COVID‐19‐related topics. A nearly identical pattern was observed in Google searches during 2020 at the global scale. The daily time series of television coverage and Internet search volumes on bats and coronavirus in the USA covaried in the first quarter of 2020, in line with the existence of a media bubble. Time‐series analysis revealed that both the Google Trends index and visits to Wikipedia pages about bats boomed in early 2020, despite the fact that this time of year is usually characterised by low search volumes.Media coverage emphasised, correctly or not, the role of bats in the COVID‐19 pandemic and amplified public interest in bats worldwide. The public image of these mammals, in many cases threatened and important ecosystem service providers, was seriously compromised. We therefore recommend that policymakers and journalists prioritise scientifically accurate communication campaigns about bats, which would help counteract the surge in bat persecution, and leverage interest towards positive human–bat interactions.

Time series forecast of sales volume based on XGBoost

Some problems such as the decline of new labor force, the increase of retired labor force emerge because of the complex and changeable market environment, consequently exacerbating the staffing problem in the retail industry. Also, the unreasonable distribution of personnel, there are few people in busy hours and too many people in idle hours, which causes waste of labor. To address this issue, we analyzed the time series of sales volume in the retail industry in detail, and processed the data with feature engineering for predicting the in-store sales volume in the future. At the same time, other features such as weather and temperature are added to improve the accuracy of the model. Considering the characteristics of the data, we choose XGBoost as the prediction model. The experiments on real-world datasets verified better performance of proposed model compared with other state-of-art models.

Modeling Non-Maturing Deposits: A Procedure for the Determination of the Minimal Time Series Length Required for Model Calibration and Back-Testing

This paper proposes a procedure for the determination of the minimal length of the historical time series of daily deposit variations in accordance with an institution’s specific risk tolerance. In a previously released paper we developed a methodology to ascertain an institutional specific confidence level to be used for the same purposes. As the formula for the determination of the confidence level depends on the length of the deposit variations time series, we complete the procedural construct by proposing a means to estimating the minimal length of the daily deposit volume variations vector in accordance with Principle 9 of the EBA recommendations for liquidity risk management best practices. As the formula determining the minimal length of the volume time series will depend on the institutional specific confidence level to be used (as described in the previous paper), we will augment our procedure with a recursive method which converges to optimal values for both parameters. The procedure centers on the identification of structural breaks in the volume dynamics distributions. The resulting vectors of constant maturities are identified via distributional power statistical tests. We further illustrate the application of the proposed procedure using a dataset of daily demand deposit volumes from a fictional European savings bank.

Gastrointestinal 4D MRI with respiratory motion correction.

Gastrointestinal motion patterns such as peristalsis and segmental contractions can alter the shape and position of the stomach and intestines with respect to other irradiated organs during radiation therapy. Unfortunately, these deformations are concealed by conventional four-dimensional (4D)-MRI techniques, which were developed to visualize respiratory motion by binning acquired data into respiratory motion states without considering the phases of GI motion. We present a method to reconstruct breathing-compensated images showing the phases of periodic gastric motion and study the effect of this motion on regional anatomical structures. Sixty-seven DCE-MRI examinations were performed on patients undergoing MRI simulation for hepatocellular carcinoma using a golden-angle stack-of-stars sequence that collected 2000 radial spokes over 5min. The collected data were reconstructed using a method with integrated respiratory motion correction into a time series of 3D image volumes without visible breathing motion. From this series, a gastric motion signal was extracted by temporal filtering of time-intensity curves in the stomach. Using this motion signal, breathing-corrected back-projection images were sorted according to the gastric phase and reconstructed into 21 gastric motion state images showing the phases of gastric motion. Reconstructed image volumes showed gastric motion states clearly with no visible breathing motion or related artifacts. The mean frequency of the gastric motion signal was 3cycles/min with a standard deviation of 0.27cycles/min. Periodic gastrointestinal motion can be visualized without confounding respiratory motion using the presented GI 4D MRI technique. GI 4D MRIs may help define internal target volumes for treatment planning, aid in planning organ at risk volume definition, or support motion model development for gastrointestinal motion tracking algorithms for real-time MR-guided radiation therapy.

Divergent Alterations of Structural–Functional Connectivity Couplings in First-episode and Chronic Schizophrenia Patients

Emerging evidence suggests that the coupling relating the structural connectivity (SC) of the brain to its functional connectivity (FC) exhibits remarkable changes during development, normal aging, and diseases. Although altered structural–functional connectivity couplings (SC–FC couplings) have been previously reported in schizophrenia patients, the alterations in SC–FC couplings of different illness stages of schizophrenia (SZ) remain largely unknown. In this study, we collected structural and resting-state functional MRI data from 73 normal controls (NCs), 61 first-episode (FeSZ) and 78 chronic (CSZ) schizophrenia patients. Positive and negative syndrome scale (PANSS) scores were assessed for all patients. Structural and functional brain networks were constructed using gray matter volume (GMV) and resting-state magnetic resonance imaging (rs-fMRI) time series measurements. At the connectivity level, the CSZ patients showed significantly increased SC–FC coupling strength compared with the FeSZ patients. At the node strength level, significant decreased SC–FC coupling strength was observed in the FeSZ patients compared to that of the NCs, and the coupling strength was positively correlated with negative PANSS scores. These results demonstrated divergent alterations of SC–FC couplings in FeSZ and CSZ patients. Our findings provide new insight into the neuropathological mechanisms underlying the developmental course of SZ.

Impact of Inlet Closures on Water Volumes in Barnegat Bay, New Jersey, U.S.A.

Slusarczyk, G.; Cialone, M.A., and Nadal-Caraballo, N.C., 2021. Impact of inlet closures on water volumes in Barnegat Bay, New Jersey, U.S.A. Journal of Coastal Research, 37(3), 461–467. Coconut Creek (Florida), ISSN 0749-0208.This paper describes the impact of Hurricane Sandy on the volume of water in Barnegat Bay, New Jersey, and the potential for inlet closures to reduce that volume, had they been in place at the time of the storm. Barnegat Bay is hydraulically connected with several other bays to form the largest bay system in New Jersey (over 326 km2). The importance of reducing the volume of water in the embayment during a storm event is to minimize back-bay flooding from the water volume itself and to mitigate additional back-bay flooding from seiching effects along the north-to-south axis of this bay system. Analyses of Barnegat Bay water volume time-series during Hurricane Sandy were made to compare water volumes for different combinations of Barnegat Bay inlet closures to water volumes with no closures in place. The storm event was numerically simulated for all inlet closure configurations utilizing the ADvanced CIRCulation model. This paper will discuss the effect of numerically “closing” three inlets that contribute to surge propagation into the bay system. The comparison is based on closing the inlets individually and in combinations.