Time Series Research Articles

Use of ARIMA Model for Forecasting Consequences Due to Traffic Crashes in the Kingdom of Saudi Arabia

Times series models are important statistical methods for analysing data recorded at points of time which considers the order of observations. In this study, the Autoregressive Integrated Moving Average (ARIMA) model was used to analyse the consequences of traffic crashes in the Kingdom of Saudi Arabia (KSA) from 2002-2022. Over the study period, there was a decreasing trend in the forecasted number of all types of injuries per 1,000 traffic crashes. Moreover, to check the validity of the fitted model, the actual observations are plotted with predicted values from 2016 to 2022 and showed a nearly equal and exact pattern between the total number of predicted values and the actual data. It is concluded that the ARIMA model is a good fit to forecast the parameter of consequences per 1,000 crashes. The decrease in consequences may be due to preventive or mitigation measures by various organisations in KSA.

Examining the nexus between carbon dioxide emissions, economic growth, fossil fuel energy use, urbanization and renewable energy towards achieving environmental sustainability in India

Purpose Global urbanization has accelerated due to the persistent trend of rural-to-urban migration in search of better prospects and livelihoods, which has had serious negative effects on the environment, especially in rapidly developing economies. Hence, the purpose of the study is to analyse the relationship between urbanization, economic growth, consumption of renewable energy and carbon emissions with careful examination, particularly in the context of India, where urban population growth has skyrocketed. Design/methodology/approach This study uses econometric methods like Granger causality analysis and the ARDL bound tests, to analyse the intricate relationships between the selected time series variables for India from 1970 to 2022. Findings This research highlights the difficult task of striking a balance between economic development and environmental preservation by emphasizing the crucial role that urbanization and economic expansion play in causing carbon emissions. India’s urbanization trajectory presents a significant policy problem that calls for a move towards renewable energy sources to successfully decrease carbon emissions. Moreover, this research indicates a two-way causal relationship between economic growth, urbanization and carbon emissions, pointing to the intricate interactions between these variables during the developmental stage. Research limitations/implications Despite India’s per capita emissions remaining below the global average, this study highlights the mounting policy challenge of balancing economic development with environmental sustainability as urbanization persists. The paper emphasizes the need for India to invest in renewable energy capacity to replace non-renewable sources and mitigate the carbon footprint of its growing energy demands. Collaborative efforts between India and the developed world to facilitate access to clean energy technologies are crucial for India to achieve sustainable growth in the long run. Originality/value To the best of the authors’ knowledge, existing literature predominantly focuses on investigating the relationship between renewable energy and economic growth, with only a limited number of studies exploring the impact on sustainable development to attain carbon neutrality. Furthermore, these studies have not considered the role of urbanization and non-renewable energy in addressing the challenge of sustainability issues in an emerging country like India. Hence, this study is a comprehensive study that addresses the research gap in these directions.

Improving the estimate of higher-order moments from lidar observations near the top of the convective boundary layer

Abstract. Ground-based lidar data have proven extremely useful for profiling the convective boundary layer (CBL). Many groups have derived higher-order moments (e.g., variance, skewness, fluxes) from high-temporal-resolution lidar data using an autocovariance approach. However, these analyses are highly uncertain near the CBL top when the depth of the CBL (zi) is changing during the analysis period. This is because the autocovariance approach is usually applied to constant height levels and the character of the eddies is changing on either side of the changing CBL top. Here, a new approach is presented wherein the autocovariance analysis is performed on a normalized height grid, with a temporally smoothed zi. Output from a large eddy simulation model demonstrates that deriving higher-order moments from time series on a normalized height grid has better agreement with the slab-averaged quantities than the moments derived from the original height grid.

Deficiency of FABP7 Triggers Premature Neural Differentiation in Idiopathic Normocephalic Autism Organoids

AbstractAutism spectrum disorder (ASD), which is caused by heterogeneous genetic and environmental factors, is characterized by diverse clinical phenotypes linked to distinct pathological mechanisms. ASD individuals with a shared clinical phenotype might contribute to revealing the molecular mechanism underlying ASD progression. Here, it is generated induced pluripotent stem cell (iPSC)‐derived cerebral organoids from normocephalic individuals with ASD in a prospective birth cohort with a shared clinical diagnosis. Multiple cell lines and time series scRNA‐seq combined with a histomorphological analysis revealed premature neural differentiation of neural stem cells (NSCs) and decreased expression of Fatty acid binding protein 7 (FABP7) in ASD organoids. It is subsequently revealed alterations in the phosphorylation levels of Mitogen‐Activated Protein Kinase Kinase 1/2 (MEK1/2), which are downstream of FABP7, and the regulation of the FABP7/MEK pathway reversed improper neural differentiation in the ASD organoids. Moreover, both Fabp7‐knockdown and MEK2‐overexpressing mice exhibited repetitive stereotyped behaviors and social defects relevant to autism. This study reveals the role of the FABP7/MEK pathway in abnormal NSC differentiation in normocephalic individuals with ASD, which might provide a promising therapeutic target for ASD treatment.

Fast implementation of least squares variance component estimation for diagonal matrices: applications to GNSS time series

Fast implementation of least squares variance component estimation for diagonal matrices: applications to GNSS time series

Integrating Multi-Source Remote Sensing Data for Forest Fire Risk Assessment

Forest fires are a frequent and destructive phenomenon in Southwestern China, posing significant threats to ecological systems and human lives and property. In response to the growing need for effective forest fire prevention, this study introduces an innovative method for predicting and assessing forest fire risk. By integrating multi-source data, including optical and microwave remote sensing, meteorological, topographic, and human activity data, the approach enhances the sensitivity of risk models to vegetation water content and other critical factors. The vegetation water content is derived from both Vegetation Optical Depth and optical remote sensing data, allowing for a more accurate assessment of changes in vegetation moisture that influence fire risk. A time series prediction model, incorporating attention mechanisms, is used to assess the probability of fire occurrence. Additionally, the method includes fire spread simulations based on Cellular Automaton and Monte Carlo approaches to evaluate potential burn areas. This combined approach can provide a comprehensive fire risk assessment using the probability of both fire occurrence and potential fire spread. Experimental results show that the integration of microwave data and attention mechanisms improves prediction accuracy by 2.8%. This method offers valuable insights for forest fire management, aiding in targeted prevention strategies and resource allocation.

The Contribution of Siliceous Plankton to Vertical Export Flux in the Eastern Mediterranean: A Comparative Study of the North Aegean, Cretan, and Ionian Seas

This study investigates the intricate dynamics of siliceous plankton species within the open marine regions of the Greek Seas, focusing on their seasonal and spatial variability. For this purpose, vertical export fluxes of diatoms (DtF), silicoflagellates (SF), and radiolaria (RF) were analyzed in three sediment trap time series obtained from the North Aegean, Cretan, and Ionian Seas. Special attention was given to diatom assemblages, resulting in the estimation of the DtF community structure and diversity for each studied site. Diatom flux values reached 353.9 × 103 valves m−2 day−1, 77.7 × 103 valves m−2 day−1, and 42.4 × 103 valves m−2 day−1 in the North Aegean, Ionian, and Cretan Seas, respectively. SF maxima were 1309.8 × 103 skeletons m−2 day−1 in the North Aegean Sea, 35.2 × 103 skeletons m−2 day−1 in the Ionian Sea, and 11.9 × 103 skeletons m−2 day−1 in the Cretan Sea (South Aegean Sea). RF values reached 13.9 × 103 radiolaria m−2 day−1, 11.9 × 103 radiolaria m−2 day−1, and 5.4 × 103 radiolaria m−2 day−1 in the North Aegean, Ionian, and Cretan Seas, respectively. The North Aegean Sea exhibited significantly higher mean total fluxes, particularly for diatoms, driven by the north-to-south oligotrophy gradient, which was influenced by riverine inflows and the nutrient-rich Black Sea water. In the Cretan and Ionian Seas, convective mixing and atmospheric deposition, especially during increased rainfall (precipitation) events, were identified as primary drivers for the increased siliceous plankton fluxes recorded in the late winter–spring months. Diatom communities were dominated by Naviculales and Fragilariales; the prevalence of the former in the North Aegean Sea is likely linked to the higher nutrient levels in its upper photic zone, as Naviculales includes species with a high affiliation to nutrient enrichment.

Effect of the uncertainty of autocorrelation estimation on trend analysis

Autocorrelation is ubiquitous in real-life time series. For trend analysis, positive autocorrelation mainly increases the statistical uncertainty of the detected trend. The theoretical framework has been established to quantify how the autocorrelation can impact trend analysis if its characteristic is known precisely. However, if the autocorrelation is also uncertain like that estimated in real-life data, then the consequences are yet unclear. The situation is further complicated by the concurrence of both short- and long-range correlation (SLRC), e.g., in air temperature. In this study, we use an appropriate minimal model with one short-range autoregressive parameter and one long-range fractional parameter to account for such SLRC, and then accordingly propose a framework to integrate the effect of the uncertainty of autocorrelation estimation into trend analysis, on the basis of the detrended fluctuation analysis and Akaike weights. This framework can also obtain a joint probability density function of the estimated autoregressive and fractional parameters for measuring their uncertainty, identifying their combined optimal estimates, uncovering their strong interdependence, and determining their two-dimensional confidence region. The effectiveness and applicability of the proposed framework are verified by a numerical experiment and a case study of daily air temperature anomalies at the Potsdam station. This study can provide a solid theoretical basis to underpin our understanding of the autocorrelation effect on trend analysis in theory and applications. Published by the American Physical Society 2024

Estimating Mediation Effects in ABAB Reversal Designs

Single-Case Experimental Designs (SCEDs), or N-of-1 trials, are commonly used to estimate intervention effects in many disciplines including in the treatment of youth mental health problems. SCEDs consist of repeated measurements of an outcome over time for a single case (e.g., student or patient) throughout one or more baseline phases and throughout one or more intervention phases. The manipulation of the baseline and intervention phase make the SCED a type of interrupted time series design, which is considered one of the most effective experimental designs for causal inference. An important step towards understanding why interventions are effective at producing a change in the outcome is through the investigation of mediating mechanisms. Hypotheses of mediating mechanisms involve an intervention variable which is hypothesized to affect an outcome through its effect on a mediating variable. Little work has attempted to combine mediation analysis and ABAB reversal designs. Therefore, the goals of this paper are to define, estimate, and interpret mediation effects for ABAB reversal designs. An empirical example is used to demonstrate how to estimate and interpret the mediation effects. R code is provided for researchers interested in estimating mediation effects in single-case reversal designs.

Summer and Winter Anomalies of Day and Night Cloud Parameters over Western Siberia Using MODIS Data and ERA5 Reanalysis During 2001–2022

We present the results of analysis of multiyear variability of cloud parameters (day and night) over Western Siberia in summer and winter during 2001-2022 based on MODIS data and ERA5 reanalysis. Three latitude zones of the target region are considered: northern (66-72° N, 68-82° E), transitional (60-65° N, 62-88° E) and southern (54-59° N, 62-88° E). We have plotted time series and on their basis we have identified trends of the following cloud parameters: fraction, top height, top pressure, top temperature and effective emissivity. It was found that in 2010, 2012, 2014 and 2016, the largest number of anomalies in the time series of the above cloud features was observed. The comparison results for the considered cloud parameters with the variability of the land surface temperature and geopotential heights at the baric levels of 500, 700, 850 and 1000 hPa are presented. We discuss hypotheses about the causes of anomalous values in time series of investigated cloud parameters at different times of day in summer and winter, related to the specific features of atmospheric circulation over Western Siberia in different years.

CAnDOIT: Causal Discovery with Observational and Interventional Data from Time Series

The study of cause and effect is of the utmost importance in many branches of science, but also for many practical applications of intelligent systems. In particular, identifying causal relationships in situations that include hidden factors is a major challenge for methods that rely solely on observational data for building causal models. This article proposes CAnDOIT, a causal discovery method to reconstruct causal models using both observational and interventional time‐series data. The use of interventional data in the causal analysis is crucial for real‐world applications, such as robotics, where the scenario is highly complex and observational data alone are often insufficient to uncover the correct causal structure. Validation of the method is performed initially on randomly generated synthetic models and subsequently on a well‐known benchmark for causal structure learning in a robotic manipulation environment. The experiments demonstrate that the approach can effectively handle data from interventions and exploit them to enhance the accuracy of the causal analysis. A Python implementation of CAnDOIT is developed and is publicly available on GitHub: https://github.com/lcastri/causalflow.

Estimating canopy cover loss with Landsat dense time series: a Mortality Magnitude Index for the Ecosystem Disturbance and Recovery Tracker (eDaRT)

Estimating canopy cover loss with Landsat dense time series: a Mortality Magnitude Index for the Ecosystem Disturbance and Recovery Tracker (eDaRT)

Abstract A038: A targetable tumor-induced wound-healing response is essential for osteosarcoma lung metastasis

Abstract Lung metastasis is responsible for nearly all deaths in children and teenagers affected by osteosarcoma, the most common pediatric sarcoma. The mechanisms by which malignant bone cells manipulate the lung microenvironment to promote the colonization and growth of metastatic tumor cells remain unclear but represent a potential therapeutic opportunity for preventing and treating metastasis. In this study, we sought to understand how osteosarcoma cells educate the lung microenvironment during metastatic progression. To address this gap, we generated several time series datasets of niche-labeled, single-cell RNA-seq of developing metastatic lesions and then used these to characterize the changes occurring in tumor and tumor-associated stromal cells during colonization. After identifying candidate mechanisms, we tested several therapeutic interventions to evaluate their effects on metastasis development in vitro using a tumor-on-lung co-culture system and in vivo using our animal models. Osteosarcoma cells induced acute epithelial injury upon lung dissemination. Shortly after dissemination, a paracrine loop established between a hyper-secretory, hypo-proliferative subpopulation of tumor cells and alveolar epithelial cells triggers the adoption of a chronic, non-resolving wound phenotype within the surrounding tissues that culminates in extensive tissue fibrosis with persistent extracellular matrix deposition, especially fibronectin. Cellular changes observed during this process include the proliferation of type 2 alveolar epithelial cells, the accumulation of highly fibrogenic epithelial intermediates (analogous to those observed in pulmonary fibrosis), and the emergence of a large population of scar-promoting macrophages. The formation of this matrix seems essential for lesion formation—disruption of the matrix-tumor cell interaction eliminated pseudonodules in vivo. Inhibition of pro-fibrotic pathways using nintedanib, an FDA- approved drug used to treat pulmonary fibrosis, or of tumor-epithelial paracrine signals with anakinra, an antagonist of IL1 signaling, effectively blocked metastatic lesion growth in both murine and human xenograft models, showing evidence of microenvironment reprogramming in the single cell data. Combining these therapies, which target primarily the hyper-secretory, hypo-proliferative “anchor” cells, with conventional therapies, which target proliferating tumor cells, proved highly effective in eliminating established metastatic lesions. Our work demonstrates that disseminated osteosarcoma cells trigger an aberrant, non-resolving wound-healing response in tumor-associated lung tissues, similar to that observed in non-malignant pulmonary fibrosis, and that this pathologic wound-healing response promotes metastatic growth. Therapeutic disruption of fibrosis-associated signaling prevents metastasis and treats established metastatic disease in multiple osteosarcoma models. Citation Format: James B. Reinecke, Leyre Jimenez Garcia, Amy C. Gross, Matthew V. Cannon, Yogesh Budhathoki, Ryan D. Roberts. A targetable tumor-induced wound-healing response is essential for osteosarcoma lung metastasis [abstract]. In: Proceedings of the AACR Special Conference in Cancer Research: Tumor-body Interactions: The Roles of Micro- and Macroenvironment in Cancer; 2024 Nov 17-20; Boston, MA. Philadelphia (PA): AACR; Cancer Res 2024;84(22_Suppl):Abstract nr A038.

Research on information leakage in time series prediction based on empirical mode decomposition.

Time series analysis predicts the future based on existing historical data and has a wide range of applications in finance, economics, meteorology, biology, engineering, and other fields. Although the combination of decomposition techniques and machine learning algorithms can effectively solve the problem of predicting nonstationary sequences, this kind of decomposition-integration-prediction strategy of the prediction method has serious defects. After the decomposition of the division of the training set and the test set, the information of the test set in the process of decomposition of the information leakage ultimately shows a high accuracy of the prediction of the illusionary. This paper proposes three improvement strategies for this type of "information leakage" problem: sliding window decomposition (SW-EMD), single training and multiple decomposition (STMP-EMD), and multiple training and multiple decomposition (MTMP-EMD). They are combined with a bidirectional multiscale temporal convolutional network (MSBTCN), bidirectional long- and short-term memory network (BiLSTM), and attention mechanism (DMAttention), which introduces a dependency matrix based on cosine similarity to be applied to water quality prediction. The experimental results show that the model achieves good performance in the prediction of three water quality indicators (pH, DO and KMnO4), and the accuracies of the three models proposed in this paper are improved by 1.958% and 0.853% in terms of the RMSE and MAPE, respectively, compared with those of the mainstream LSTM models. The key contributions of this study include the following: (1) three methods are proposed to improve the class EMD decomposition, which can effectively solve the problem of "information leakage" that exists in the current models via class EMD decomposition; (2) the CEEMDAN-MSBTCN-BiLSTM-DMAttention model structure is innovated by combining improved class EMD decomposition methods; and (3) the three improved decomposition methods proposed in this paper can effectively solve the problem of "information leakage" and optimize the prediction model at the same time. This study provides an effective experimental method for water quality prediction and can effectively address the problem of "overfitting" models via class EMD decompositions during model training and testing.

Causal discovery from nonstationary time series

AbstractThis paper introduces a new causal structure learning method for nonstationary time series data, a common data type found in fields such as finance, economics, healthcare, and environmental science. Our work builds upon the constraint-based causal discovery from nonstationary data algorithm (CD-NOD). We introduce a refined version (CD-NOTS) which is designed specifically to account for lagged dependencies in time series data. We compare the performance of different algorithmic choices, such as the type of conditional independence test and the significance level, to help select the best hyperparameters given various scenarios of sample size, problem dimensionality, and availability of computational resources. Using the results from the simulated data, we apply CD-NOTS to a broad range of real-world financial applications in order to identify causal connections among nonstationary time series data, thereby illustrating applications in factor-based investing, portfolio diversification, and comprehension of market dynamics.

Adaptive compensation using long short‐term memory networks for improved control performance in real‐time hybrid simulation

AbstractReal‐time hybrid simulation (RTHS) divides structural systems into numerical and experimental substructures, providing a cost‐effective solution for analyzing structural systems, especially those that are large or complex. However, the actuation systems between these substructures inevitably introduce delays, affecting the stability and accuracy of RTHS. To address this issue, this study proposes an adaptive compensation method based on a conditional adaptive time series (CATS) compensator and a long short‐term memory (LSTM) network, termed CATS‐LSTM. The LSTM model predicts actuator responses for parameter estimation and calculates prediction errors, improving control performance and reducing delays. The effectiveness of the proposed CATS‐LSTM method and the accuracy of the LSTM prediction are validated through a series of simulations and experiments. The results indicate that the proposed CATS‐LSTM method outperforms both the CATS and phase lead (PL) methods. Compared to the CATS method, the proposed method reduces the maximum delay, root mean square error, and peak error by 3 ms, 3.66%, and 4.78%, respectively, while achieving reductions of 12 ms, 8.4%, and 10.05%, compared to the PL method. Furthermore, the CATS‐LSTM method is significantly less sensitive to initial parameter estimates, compared to the CATS method, enhancing robustness and mitigating the effects of inaccurate or varying initial parameter estimates.

The Association of Drought with Different Precipitation Grades in the Inner Mongolia Region of Northern China

Drought has become an important factor affecting the environment and socio-economic sustainable development in northern China due to climate change. This study utilized the Standardized Precipitation Index (SPI) as a drought metric to investigate the correlation between drought characteristics and different grades of precipitation and rain days. The analysis was based on a long-term time series of precipitation data obtained from 116 meteorological stations located in Inner Mongolia, spanning 1960 to 2019. To achieve the objectives of the current research, the daily precipitation was categorized into four grades based on the “24-h Precipitation Classification Standard”, and the frequency of rain days for each grade was determined. Subsequently, the SPI was calculated for 1 and 12 months, enabling the identification of drought events. The results revealed pronounced spatiotemporal regional variations and complexities in the dry–wet climatic patterns of Inner Mongolia, with significant decreases in precipitation emerging as the primary driver of drought occurrences. Approximately 6% of the entire study period experienced short-term drought, while long-term drought periods ranged from 23% to 38%. Regarding multi-year trends, precipitation exhibited a weak increasing trend, while rain days exhibited a weak decreasing trend. Drought exhibited an alleviating trend, with 92% of stations displaying coefficients > 0 for SPI_Month and over 62% of stations displaying coefficients > 0 for SPI_Year. At the monthly scale, drought was most correlated with light rainfall trends and least correlated with moderate rainfall trends. At the annual scale, drought was relatively highly correlated with moderate and heavy rainfall distributions but poorly correlated with light rainfall. The results suggested that achieving the precise monitoring and mitigation of drought disasters in Inner Mongolia in the future will require a combined analysis of indicators, including agricultural drought, hydrological drought, and socio-economic drought. Such an approach will enable a comprehensive analysis of drought characteristics under different underlying surface conditions in Inner Mongolia.

Change-point detection in functional time series: applications to age-specific mortality and fertility

Change-point detection in functional time series: applications to age-specific mortality and fertility

The relationship between crustal deformation characteristics and strong earthquakes in western China revealed by GNSS.

To clarify the relationship between GNSS (Global Navigation Satellite System) crustal deformation and the preparation of strong earthquakes in the western Chinese Mainland, a series of methods including velocity field, strain rate field, and strain parameter time series are adopted based on the GNSS observation data from the CMONOC (Crustal Movement Observation Network of China). The abnormal characteristics of crustal deformation movement before the M ≥ 5 earthquakes in the area since 1999 are studied. The corresponding variety of the crustal movement before earthquakes is discussed in combination with the seismogenic structure of earthquakes. The abnormalities of the GNSS velocity field, strain field, and strain parameter time series before earthquakes with different magnitudes are proposed. Finally, the anomaly criterion before medium-strong-large earthquakes in the western Chinese Mainland is proposed according to the GNSS and its combination with the characteristics of geological structure. The results can provide a basis for the forecasting of the trend and location of medium-strong-large earthquakes in the western Chinese Mainland.

Analysis of cutaneous leishmaniasis among military personnel in the Islamic Republic of Iran: a spatiotemporal study between 2018 and 2022, trend forecasting based on ARIMA model.

Cutaneous leishmaniasis is one of the few infectious diseases whose global prevalence is on the rise. Iran ranks among the eight most affected countries in the world. Iranian military personnel are often sent to endemic areas for cutaneous leishmaniasis without prior immunity, and they have fewer health facilities in military centers than the general population. This study aims to comprehensively investigate the situation of cutaneous leishmaniasis in Iranian military personnel across all units from 2018 to 2022 and predict the disease trend using time series analysis up to the end of 2025. We analyzed data from the Iranian Ministry of Health to perform spatiotemporal and descriptive analyses based on patient frequency. Variables examined included age distribution, cutaneous leishmaniasis types (zoonotic or anthroponotic), month of healthcare facility visits, and lesion locations. This study employed the ARIMA model (p = 2, d = 0, q = 1)(P = 3, D = 0, Q = 0), for time series analysis and forecasting the disease trend up to 36 months after 2022. Over five years, 2,894 patients were reported. The Esfahan, Khuzestan, and Ilam provinces had the highest average patient counts, with hot spots primarily found in central, south, southwestern, and western Iran. Although the total number of patients with zoonotic cutaneous leishmaniasis was almost equal to anthroponotic cutaneous leishmaniasis, in high-risk provinces such as Esfahan, Khuzestan, and Ilam, the confirmed cases of zoonotic cutaneous leishmaniasis were much more than anthroponotic cutaneous leishmaniasis. patient numbers peak in October and November. Demographic analysis revealed that younger patients outnumbered older patients. Lesion locations were frequent on the forelimbs and lower limbs. The time series analysis for 36 months after 2022 indicated the seasonal pattern of the disease and predicted an upward trend after 2022. While overall cases have declined, provinces such as Esfahan exhibit an upward trend. The expansion of hotspots from the west and southwestern to the center and south of Iran, coupled with an increasing trend in time series analysis, suggests the potential emergence of new foci and a rise in patient numbers in the future. In provinces with high disease prevalence, preventive measures should be prioritized, particularly in Ilam, Khuzestan, and Esfahan.