Time Series Analysis Research Articles

Persistent Behavior in Energetic Neutral Atom Time Series from IBEX

We investigate the long-term persistence of the time series of energetic neutral atom (ENA) fluxes recorded by the Interstellar Boundary Explorer (IBEX). ENAs provide global information about the outer heliosphere and its interactions with the very local interstellar medium. To avoid the IBEX Ribbon, here, we focus our analysis solely on the polar regions N60°–N90° and S60°–S90°. Each year, IBEX takes two measurements of every pixel in the sky. We make use of the whole set of 14 yr of IBEX data and adhere to the correct time order for the construction of the flux time series. We examine in detail both the trend and the fluctuations of these time series. Using modern methods of time series analysis and persistence characterization, we show that the time series (i) trend is influenced by the solar cycle, (ii) persistence can be established independently of the presence of this trend, and (iii) statistical properties of the fluctuations differs between north and south, pointing to the existence of anisotropy and thus a north–south asymmetry.

Long-Term Pattern of Psychotropic Medication Uses Among Swedish Parents of Children Diagnosed With Cancer.

Parents experience a range of mental health disorders following their child's cancer diagnosis. It is thus essential to explore how the child's cancer diagnosis affects the dosage of psychotropic medication in both short- and long-term use, as well as potential disparities in utilization patterns between mothers and fathers. Using Swedish registers, we identified all parents whose children were diagnosed with cancer from July 2009 to December 2015 in Sweden. We randomly matched up to 5 parents of cancer-free children conditional on their baseline characteristics. All parents were followed up from 4 years before to 4 years after their child's diagnosis. Psychotropic medication cumulative defined daily doses (DDDs) were retrieved from the Swedish Prescribed Drug Register. Using interrupted time series analyses, we sought to investigate how childhood cancer diagnosis influences the evolving pattern of parental psychotropic medication dosage across time with consideration for potential differences compared with matched parents before the child's cancer diagnosis. We calculated the attributable proportion due to the diagnosis of childhood cancer. Following a child's cancer diagnosis, mothers experienced a steady increase in psychotropic medication use, averaging 4.9 DDDs per year compared with matched comparisons, with a 46.0% adjusted attributable proportion in the initial year. Fathers had an abrupt increase in psychotropic medication use in the first year after diagnosis, with an adjusted attributable proportion of 72.1%. Parents with lower education attainment tended to use more psychotropic medication. In response to a child's cancer diagnosis, parents showed increased use of psychotropic medication compared with matched comparisons. Additionally, utilization patterns differed between mothers and fathers. Timely prevention and early support for parents are needed to alleviate their psychological challenges, potentially mitigating the strain on medical resources associated with increased psychotropic medication use.

Assessing the impact of the National Clean Air Programme in Uttar Pradesh's non-attainment cities: a prophet model time series analysis

BackgroundUttar Pradesh, India’s largest state, faces critical pollution levels, necessitating urgent action. The National Clean Air Programme (NCAP) targets a 40% reduction in particulate pollution by 2026. This study assesses the NCAP’s impact on 15 non-attainment cities in Uttar Pradesh using the Prophet forecasting model. MethodsMonthly data from 2016 to 2023 on AQI and PM10 concentrations were sourced from the Uttar Pradesh Pollution Control Board. Significant changes in mean AQI and PM10 levels from 2017 to 2023 were evaluated using the Friedman Test. Prophet models forecasted PM10 concentrations for 2025-26, with relative percentage changes calculated and model evaluation metrics assessed. FindingsMost cities exhibited unhealthy air quality. Jhansi had the lowest AQI (72·73) in 2023, classified as 'moderate' by WHO standards. Gorakhpur consistently showed 'poor' AQI levels, peaking at 249·31 in 2019. Western Uttar Pradesh cities such as Ghaziabad, Noida, and Moradabad had significant pollution burdens. Predictions showed Bareilly with over a 70% reduction in PM10 levels, Raebareli 58%, Moradabad 55%, Ghaziabad 48%, Agra around 41%, and Varanasi 40%, meeting NCAP targets. However, Gorakhpur and Prayagraj predicted increases in PM10 levels by 50% and 32%, respectively. Moradabad’s model showed the best performance with an R2 of 0·81, MAE of 17·27 μg/m3, and MAPE of 0·10. InterpretationForecasting PM10 concentrations in Uttar Pradesh's non-attainment cities offers policymakers substantial evidence to enhance current efforts. While existing measures are in place, our findings suggest that intensified provisions may be necessary for cities predicted to fall short of meeting program targets. The Prophet model's forecasts can pinpoint these at-risk areas, allowing for targeted interventions and regional adjustments to strategies. This approach will help promote sustainable development customized to each city's specific needs. FundingNo funding was issued for this research.

An interrupted time series analysis of fentanyl, naloxone, and opioid-involved deaths in five counties in Eastern Missouri

IntroductionRates of opioid overdose deaths (OOD) have increased since the introduction of illicitly manufactured fentanyl in the U.S. drug supply. Though community-based naloxone distribution efforts have been found to effectively reduce OOD, no studies to date have examined their effects during the fentanyl era, nor in the epicenter of a state's overdose crisis. Thus, the current study assessed the impacts of both fentanyl and of subsequent grant-funded community-based naloxone distribution on OOD across Missouri's St. Louis region. MethodsMedical examiner data includes individuals who died within one of the five study counties (St. Louis City, St. Louis County, and Franklin, Jefferson, St. Charles counties [combined as “Collar Counties”]) between 2011 and 2022 due to an opioid overdose (N = 6799). Naloxone distribution date and location data came from Missouri's university-based naloxone distribution team. We conducted a controlled interrupted time series using an autoregressive model via proc. ARIMA to examine changes over time in the rate of OOD associated with the introduction of fentanyl (defined to be present in 25 % of opioid overdose deaths; June 2015) and the start of naloxone distribution for each location (August 2017, October 2017, and January 2018, respectively). ResultsThe introduction of fentanyl into the drug supply of St. Louis City was associated with an increased rate of OOD over time (p < .001). Naloxone distribution in the City was associated with an immediate decrease in OOD (p < .001) followed by a slowed increase in OOD (p < .001). These findings were not replicated in St. Louis County nor the Collar Counties. ConclusionsThe effects of fentanyl and naloxone on OOD varied by region in Eastern Missouri. Specifically, fentanyl and naloxone effects were only found in a high-need urban area with existing harm reduction and street outreach programs, but not in surrounding suburban or rural areas. State-level naloxone distribution decision-makers should prioritize funding harm reduction services in both urban and non-urban settings to help increase targeted naloxone distribution to those actively using drugs. Additionally, interventions tailored for people who engage in solitary drug use - such as safe consumption spaces and overdose detection technologies - should receive increased investment and implementation, particularly in rural areas where harm reduction infrastructure is more scarce.

Spatial associations of daily PM2.5 concentration with cardiovascular and pulmonary morbidity in Korea

Air pollution, particularly fine particulate matter less than 2.5 μm in diameter (PM2.5), contributes to respiratory and cardiovascular diseases and poses significant public health risks worldwide. This study evaluated the short-term effects of PM2.5 on hospital admissions for cardiovascular and respiratory diseases, with additional analyses to identify vulnerable populations based on regional characteristics.The present study analyzed data from 249 Korean communities between 2006 and 2021. Data on daily hospital admissions for cardiovascular and respiratory diseases were obtained from the National Health Insurance Service. Data on PM2.5 concentrations were sourced from air quality modeling. Additional data on regional characteristics, including the regional deprivation index, proportion of elderly residents, education levels, and greenness levels, were also collected. We used case time series analysis to assess the associations between PM2.5 concentrations and hospital admissions for cardiovascular and respiratory diseases and explored effect modification by regional characteristics with stratified analyses.The mean numbers of daily cardiovascular admissions and respiratory admissions were 5.68 ± 5.46 and 6.46 ± 8.03, respectively. The mean PM2.5 concentrations were 23.58 ± 13.66 μg/m3. A10 μg/m³ increment in daily PM2.5 concentration was associated with increase of cardiovascular and respiratory hospitalization by 0.94% (95% CI: 0.84%, 1.04%) and 1.43% (95% CI: 1.34%, 1.52%), respectively. Regional characteristics analysis showed significant disparities, with higher risks for hospital admissions in areas with lower deprivation and low greenness.This study highlights the significant short-term health impacts of PM2.5 on respiratory and cardiovascular hospital admissions in Korean communities. The findings underscore the critical role of regional and demographic factors in modulating these effects, identifying socio-economic areas, age structure of the population, lower education levels, and low greenness as key vulnerability factors.

A JWST Medium-resolution MIRI Spectrum and Models of the Type Ia Supernova 2021aefx at +415 days

We present a JWST MIRI medium-resolution spectrometer spectrum (5–27 μm) of the Type Ia supernova (SN Ia) SN 2021aefx at +415 days past B-band maximum. The spectrum, which was obtained during the iron-dominated nebular phase, has been analyzed in combination with previous JWST observations of SN 2021aefx to provide the first JWST time series analysis of an SN Ia. We find that the temporal evolution of the [Co iii] 11.888 μm feature directly traces the decay of 56Co. The spectra, line profiles, and their evolution are analyzed with off-center delayed-detonation models. Best fits were obtained with white dwarf (WD) central densities of ρ c = 0.9−1.1 × 109 g cm−3, a WD mass of M WD = 1.33–1.35 M ⊙, a WD magnetic field of ≈106 G, and an off-center deflagration-to-detonation transition at ≈0.5 M ⊙ seen opposite to the line of sight of the observer (−30°). The inner electron capture core is dominated by energy deposition from γ-rays, whereas a broader region is dominated by positron deposition, placing SN 2021aefx at +415 days in the transitional phase of the evolution to the positron-dominated regime. The formerly “flat-tilted” profile at 9 μm now has a significant contribution from [Ni iv], [Fe ii], and [Fe iii] and less from [Ar iii], which alters the shape of the feature as positrons mostly excite the low-velocity Ar. Overall, the strength of the stable Ni features in the spectrum is dominated by positron transport rather than the Ni mass. Based on multidimensional models, our analysis is consistent with a single-spot, close-to-central ignition with an indication of a preexisting turbulent velocity field and excludes a multiple-spot, off-center ignition.

High-resolution sedimentary cyclostratigraphy and astronomical signals in the Upper Ordovician of Southwest China

During the Late Ordovician, a series of significant biotic and geological events occurred globally and more specifically in South China. The development of a high-resolution orbital cyclostratigraphic framework and a precise radioisotope dating has greatly enhanced our understanding of the factors controlling these events. In this study, quantitative proxies for palaeo-water depths, derived from sedimentological analyses, serve as indicators for orbital cyclostratigraphic assessment.Relatively shallow-marine carbonates with well-developed shallowing-upward cycles were deposited in the western Yangtze Platform during the Late Ordovician. High-resolution geochronological data were obtained from high-precision isotope dilution thermal ionization mass spectrometry (ID-TIMS) zircon dating of K-bentonite beds at the studied section. This facilitated sedimentary facies and environmental analyses, as well as the recognition of sedimentary cycles. Based on mud content, bioclastic content, and laminations, four sedimentary facies, twelve sedimentary microfacies, and forty-eight shallowing-upward sedimentary cycles were identified in the Daduhe Formation.Through comparative time series analysis of depth rank, lime mudstone-calcareous mudstone binary data, non-carbonate content, and anhysteretic remanent magnetization (ARM), astronomical cycles and trends in sedimentation rates have been identified. This study confirms the reliability of radioisotope dating, establishing a 0.46 Myr duration for the Dicellograptus complexus graptolite biozone and a 0.96 Myr duration for the Paraorthograptus pacificus graptolite biozone, both with an empirical uncertainty of 0.1 Myr. This research proposes a reliable method for identifying astronomical cycles through high-precision sedimentary microfacies and cyclostratigraphy, in comparison with rock magnetic cyclostratigraphy studies.

Modeling Electricity Demand in UAE: Revisit of Time Series Analysis

Modeling Electricity Demand in UAE: Revisit of Time Series Analysis

Proactive total diet replacement referral for type 2 diabetes: A service evaluation

AimsType 2 Diabetes Mellitus morbidity disproportionally effects the most socioeconomically deprived 20 % of England. Total Diet Replacement (TDR) is a high impact intervention currently commissioned by Integrated Care Boards (ICB). Finding and referring eligible, motivated patients is a significant challenge. This study evaluates a 12-month population health management, proactive referral intervention commissioned by Nottingham and Nottinghamshire ICB during a 2-year TDR pilot designed to supplement referrals from primary care. MethodsA Diabetes specialist nurse clinically reviewed a list of potentially eligible patients found by searching routinely collected health care data from 16 primary care centres in 3 PCNs. Results19.7 % (50/254) of potential patients were referred during the intervention period. 19.3 % (49/254) were uncontactable. 39.8 % (101/254) declined referral. 17.7 % (45/254) were ineligible after clinical review. An interrupted time series analysis suggests monthly referrals increased during the study period due to the intervetion (F (1,22) = 5.19 p=0.0345). Searching for patients from more socioeconomically deprived areas (index of multiple deprivation deciles 1–3) did not meaningfully alter the referral yield (19.6 %, 19/97). ConclusionsProactive care interventions could be used to facilitate referrals to the TDR or other high impact interventions by healthcare commissioning bodies.

External Debt and Economic Growth of BRICS Nations: How can BRICS Integration help them?

This study examines the relationship between external debt and economic growth in the BRICS countries. It explores how BRICS integration can aid these economies and newly joined countries in solving the debt problem. Using the latest panel data techniques and country experiences, we employed the panel mean group method (PMG) and interrupted time series analysis (ITSA) for South Africa, who joined in 2010. Results show a negative relationship between external debt and economic growth at the group level, with mixed results for individual countries. Using individual country experiences, valid implications are drawn for debt-stressed countries like Ethiopia and oil-rich nations like Saudi Arabia. Integrating new member countries in BRICS can overhaul the existing system towards broader perspectives of reforms in global financial institutions. Integration into the BRICS association can benefit these countries through reforms in global financial institutions, fairer debt restructuring, joint investment projects, macroeconomic coordination, debt management knowledge sharing, innovative financing, and collective advocacy for debt relief to enhance stability and resilience.

The impact of sodium glucose co-transporter 2 inhibitors and glucagon-like peptide 1 receptor agonists on insulin utilisation and costs in Australia: a national retrospective observational cross-sectional study

Global insulin requirements for type 2 diabetes were predicted to increase by more than 20% from 2018 to 2030. However, this did not anticipate the rapid increase in use of glucagon-like peptide-1 receptor agonists and sodium-glucose cotransporter 2 inhibitors that has occurred over recent years. The current study aims to examine changes in insulin utilisation and costs in Australia from 2003 to2023. We conducted a large-scale observational study of national insulin utilisation and expenditure in Australia from 2003 to 2023 using the Australian Pharmaceutical Benefits Scheme. The proportion of insulin-treated people with type 2 diabetes between 2013 and 2023 was estimated using National Diabetes Services Scheme data. Joinpoint models and interrupted time series analysis were used to examine utilisation trends. Insulin utilisation (units of insulin per person with diabetes) increased by an average of 2.71% per annum (95% CI 1.97, 3.73) from 2003 to 2015, then fell by 2.70% per annum (95% CI-4.55,-1.39) from 2015 to 2023. The proportion of insulin-treated people with type 2 diabetes increased by 1.00% per annum (95% CI 0.81, 1.25) from 2013 to 2020, then fell by 0.66% per annum (95% CI-1.62,-0.04) from 2020 to 2023. A 43% reduction in inflation-adjusted insulin expenditure was observed between 2015 and 2023 due to a combination of reduced utilisation and reduction in the price of insulin glargine. Projected global insulin requirements and costs may be less than previously anticipated if reduced use of insulin in Australia is similarly observed in other countries. No funding was received for this study.

The Impact of the COVID-19 Pandemic and the iPLEDGE REMS Update on Isotretinoin Prescription Delays and Interruptions: An Interrupted Time Series Analysis.

The Impact of the COVID-19 Pandemic and the iPLEDGE REMS Update on Isotretinoin Prescription Delays and Interruptions: An Interrupted Time Series Analysis.

The impact of the COVID-19 pandemic on the dispensing of systemic anti-cancer therapy (SACT) in Ireland: a population based study

ObjectivesThe COVID-19 pandemic had considerable implications for cancer related care. This study aimed to examine its impact on the dispensing of systemic anti-cancer therapy (SACT) in Ireland. Study designA repeated cross-sectional design was used which involved a quasi-experimental interrupted time series analysis (ITSA), and autoregressive integrated moving average (ARIMA) models. MethodsThis nationally representative study utilised monthly pharmacy claims (i.e. dispensing) data from community and hospital schemes. Dispensed items among individuals prescribed any SACT from January 2019 to April 2021 were included. ResultsDuring the study period, 641,273 SACT items were dispensed, including 57,199 chemotherapeutic agents (8.9%), 15,970 immunotherapeutic agents (2.5%), 87,813 targeted therapies (13.7%), and 480,291 (74.9%) endocrine therapies. There were on average 3.3 and 4.1 fewer immunotherapy and targeted therapy agents, respectively, dispensed per 100,000 population per month post-March 2020 (vs. expected), compared to the level prior to March 2020. For endocrine therapy, there was a significant slowing of the trend post-March 2020 compared to the pre-pandemic period (slope change = -1.72, 95% CI -2.9 to -0.5; p<0.01). ConclusionThere was a significant level decrease in the dispensing of immunotherapy and targeted therapy during the first year of the pandemic, and a slowing of the trend for endocrine therapies. However, no differences in the dispensing of other SACT were observed.

Gaussian process regression as a powerful tool for analysing time series in environmental geochemistry

Monitoring programs require more advanced data management for the registered time series. Classical temporal series decomposition cannot fulfil current needs regarding adequate data representation, optimization of the spatial-temporal sampling resolution and predictive power. In the manuscript at hand, we will demonstrate that Gaussian process regression (GPR) models are a vital machine-learning tool to interpret temporal series, improving understanding of geochemical cycles, providing input data for geochemical models and acting as a guide for future decisions in environmental monitoring. Firstly, we explore the impacts of sampling frequency in the GPR performance for temporal series with variable lengths and sampled frequencies of water discharges. On a second approach, we present the strengths and weaknesses between classical decomposition of temporal series and GPR results for a case study: a 14-year record of water discharge, suspended particulate matter and antimony concentrations in the Garonne River. Our results suggest that (i) even short temporal series with low sampling resolution can be accurately characterized by GPR when presenting well defined seasonal patterns, and (ii) GPR provides more detailed and robust support than classical statistics to identify processes responsible for multi-scale geochemical signals. This work provides a reference for researchers, engineers, and stakeholders for more reliable monitoring, understanding, and managing aquatic ecosystems.

An approach for built-up area extraction using different indices and deep neural network (DNN) model

Mapping urbanization and built-up areas from remotely sensed data poses a formidable challenge, particularly when the spectral reflectance of built-up regions intersects with other land types. To address this, numerous spectral indices have been developed. This paper utilizes multiple indices: Normalized Difference Built-up Index (NDBI), Built-up Area Extraction Index (BAEI), Normalized Built-up Area Index (NBAI), New Built-up Index (NBI), Modified Built-up Index (MBI), Band Ratio for Built-up Area (BRBA), and Normalized Difference Vegetation Index (NDVI) to delineate built-up regions. An intersection approach between BAEI, NBAI, and NDVI refines the methodology, resulting in a final built-up map with 92.5 % accuracy and a 0.848 Kappa coefficient. Subsequently, a Deep Neural Network (DNN) model trained on this map achieves over 95 % accuracy in predicting built-up areas from Landsat 5 imagery, and the resultant built-up map achieved an overall accuracy of 92 % and a Kappa coefficient of 0.85. The proposed methodology demonstrates efficiency for time-series analysis and addresses misclassification in built-up areas. Moreover, the optimization of the DNN model proves effective when meticulous training and validation processes incorporate more precise sample datasets.

Effects of the Affordable Care Act on Contraception, Pregnancy, and Pregnancy Termination Rates.

To investigate the effects of the Affordable Care Act (ACA) and its elimination of cost-sharing on contraception utilization, pregnancy rates, and abortion rates. We conducted a retrospective cohort study within a healthcare system serving over 4.5 million insured members across 21 medical centers and 250 clinics. The study included women aged 18-45 with continuous health plan membership for at least two years in the pre-ACA (2007-2012) and post-ACA (2013-2018) periods. We analyzed out-of-pocket (OOP) costs for contraception, including oral contraceptives and long-acting reversible contraception (LARC), before and after the ACA's implementation. We then examined how the elimination of OOP costs affected contraception use, pregnancy rates, and abortion rates. The study identified 1,523,962 women of childbearing age. In 2013, cost sharing for contraception sharply declined, with average annual OOP costs dropping from $88-$94 pre-ACA to nearly zero post-ACA. Contraceptive use increased overall, rising from 30.2% pre-ACA to 31.9% by the study's end, with a notable rise in LARC use. In interrupted time-series analyses, while contraception use continued to increase post-ACA, new pregnancy rates declined at a faster rate than pre-ACA, and abortion rates continued to fall, though at a slightly slower pace than pre-ACA (p<0.05 for all trends). The Affordable Care Act's elimination of contraception cost-sharing led to increased contraception use, particularly LARC methods, and contributed to declines in both pregnancy and abortion rates. This suggests that improving access to effective contraception is a key strategy in reducing unintended pregnancies.

A Systematic Analysis of Star Cluster Disruption by Tidal Shocks. II. Predicting Star Cluster Dissolution Rates from a Time-series Analysis of Their Tidal Histories

Most of the dynamical mass loss from star clusters is thought to be caused by the time variability of the tidal field (“tidal shocks”). Systematic studies of tidal shocks have been hampered by the fact that each tidal history is unique, implying both a reproducibility and a generalization problem. Here we address these issues by investigating how star cluster evolution depends on the statistical properties of its tidal history. We run a large suite of direct N-body simulations of clusters with tidal histories generated from power spectra of a given slope and with different normalizations, which determine the timescales and amplitudes of the shocks, respectively. At fixed normalization (i.e., the same median tidal field strength), the dissolution timescale is nearly independent of the power spectrum slope. However, the dispersion in dissolution timescales, obtained by repeating simulations for different realizations of statistically identical tidal histories, increases with the power spectrum slope. This result means that clusters experiencing high-frequency shocks have more similar mass-loss histories than clusters experiencing low-frequency shocks. The density–mass relationship of the simulated clusters follows a power law with slope between 1.08 and 1.45, except for the lowest normalizations (for which clusters effectively evolve in a static tidal field). Our findings suggest that star cluster evolution can be described statistically from a time-series analysis of its tidal history, which is an important simplification for describing the evolution of the star cluster population during galaxy formation and evolution.

Dynamics of a delayed discrete-time predator prey model proposed from a nonstandard finite difference scheme

Existing literature established stability in a delayed logistic Lotka–Volterra predator- prey model in terms of equilibrium analysis. However, several researchers did not construct the time-series analysis in such models. It has also been observed that both the RK4 methods and the inbuilt ‘dde23’ MATLAB solver were unable to generate stable solutions. This motivated us to develop a nonstandard scheme to capture the numerical solutions which are well consistent with the analytical equilibrium analysis of continuous delayed predator–prey model. In this paper, we will propose a nonstandard finite difference (NSFD) scheme for a delayed predator–prey model. We shall prove that the developed scheme preserves the qualitative behavior of the system, including the local stability of the equilibrium, and stability switching for any step size h=1m,m∈Z+. It is observed that the discretized system shows the occurrence of a Neimark-Sacker bifurcation. Moreover, the convergence analysis of the numerical scheme establishes first-order convergence. The bifurcation diagram and comparison of delay τ−sequence generated by NSFD with the ones obtained by analytical means have been discussed graphically.

An Empirical Algorithm for Estimating the Absorption of Colored Dissolved Organic Matter from Sentinel-2 (MSI) and Landsat-8 (OLI) Observations of Coastal Waters

Sentinel-2/MSI and Landsat-8/OLI sensors enable the mapping of ocean color-related bio-optical parameters of surface coastal and inland waters. While many algorithms have been developed to estimate the Chlorophyll-a concentration, Chl-a, and the suspended particulate matter, SPM, from OLI and MSI data, the absorption by colored dissolved organic matter, acdom, a key parameter to monitor the concentration of dissolved organic matter, has received less attention. Herein we present an inverse model (hereafter referred to as AquaCDOM) for estimating acdom at the wavelength 412 nm (acdom (412)), within the surface layer of coastal waters, from measurements of ocean remote sensing reflectance, Rrs (λ), for these two high spatial resolution (around 20 m) sensors. Combined with a water class-based approach, several empirical algorithms were tested on a mixed dataset of synthetic and in situ data collected from global coastal waters. The selection of the final algorithms was performed with an independent validation dataset, using in situ, synthetic, and satellite Rrs (λ) measurements, but also by testing their respective sensitivity to typical noise introduced by atmospheric correction algorithms. It was found that the proposed algorithms could estimate acdom (412) with a median absolute percentage difference of ~30% and a median bias of 0.002 m−1 from the in situ and synthetic datasets. While similar performances have been shown with two other algorithms based on different methodological developments, we have shown that AquaCDOM is much less sensitive to atmospheric correction uncertainties, mainly due to the use of band ratios in its formulation. After the application of the top-of-atmosphere gains and of the same atmospheric correction algorithm, excellent agreement has been found between the OLI- and MSI-derived acdom (412) values for various coastal areas, enabling the application of these algorithms for time series analysis. An example application of our algorithms for the time series analysis of acdom (412) is provided for a coastal transect in the south of Vietnam.

Research on UK Unemployment Rate Forecast Based on ARIMA Model

The objective of this study is to predict the unemployment rate in the UK and the Eurozone for the years 2014 to 2024 using the time series analysis model known as ARIMA. It is essential to be informed of changes to employment rates in order to address them and changes in the economic environment including historical occurrences such as Brexit, COVID-19, and shifts in the labor market. The ARIMA model was used because it fits non-stationary time series, which is a feature present in the employment rates of both time series. The monthly unemployment rates for each state were seasonally adjusted and the ARIMA parameters were estimated by the automatic method. The study observed that while the interior portion of the trading cycle was effectively predicted by the ARIMA model, it failed to do so for the short-term UK unemployment in the post-2023 period because of exogenous shocks. However, the model was equally successful in predicting a marginal contraction in unemployment rates in the Euro zone after 2023. By performing the Ljung-Box test, it was established that the residuals of the models were random and therefore, the models were fit. Accordingly, the results suggest that the estimates derived from the ARIMA model are beneficial in economic predictions, which helps governments to design better social security programs and other economic plans to address the changing employment landscape.