Application Of Time Series Analysis Research Articles

Tracking the impact of typhoons on maize growth and recovery using Sentinel-1 and Sentinel-2 data: A case study of Northeast China

The increasing frequency of typhoon events, attributed to global climate change, has significantly affected agricultural production, predominantly resulting in substantial negative consequences. Accurate and timely assessment of crop damage is crucial for understanding economic implications, devising effective agricultural strategies, and enhancing resilience amid mounting climate uncertainties. This study investigates the utility of Sentinel-1 Synthetic Aperture Radar (SAR) and Sentinel-2 MultiSpectral Instrument (MSI) data in tracking maize damage severity following typhoon events. By employing continuous field sampling techniques and conducting visual interpretation of high-resolution remote sensing imagery, sample sets representing a spectrum of maize damage severity were systematically established. The application of time series analysis on Sentinel data enables a comprehensive exploration of spectral and polarization responses, providing insights into the correlation with maize damage severity. Segmentation of the maize damage timeline into pre-disaster, disaster, and recovery periods, coupled with optimization of relevant feature parameters, was undertaken to bolster monitoring precision. Leveraging the Google Earth Engine (GEE) cloud platform, a Random Forest algorithm was used to develop a model for monitoring maize damage severity across different post-typhoon periods, yielding maps delineating the distribution and magnitude of maize damage in Northeast China. Results indicate that integrating spectral indices from the pre-disaster phase with backscatter variations of polarization bands during various post-typhoon periods enhances maize damage assessment. Maize damage severity is notably elevated during the disaster period, achieving an overall accuracy of 87.22 %. While mitigated during the recovery phase, localized exacerbation occurs in severely affected regions, yielding an overall accuracy of 88.54 %. Analysis incorporating terrain and meteorological data reveals that post-typhoon maize disasters predominantly occur in low-lying and flat areas, with meteorological factors, particularly maximum wind speed and daily cumulative precipitation, exerting significant influence on damage severity. This study underscores the critical role of SAR and optical data fusion in elucidating typhoon-induced crop damage dynamics, thereby providing essential insights for proactive mitigation strategies against future agricultural losses.

Core Potentials: The Consensus Segmentation Conjecture

Segmentations are partitions of an ordered set into non-overlapping intervals. The Consensus Segmentation or Segmentation Aggregation problem is a special case of the median problems with applications in time series analysis and computational biology. A wide range of dissimilarity measures for segmentations can be expressed in terms of potentials, a special type of set-functions. In this contribution, we shed more light on the properties of potentials, and how such properties affect the solutions of the Consensus Segmentation problem. In particular, we disprove a conjecture stated in 2021, and we provide further insights into the theoretical foundations of the problem.

Optimizing Retail Inventory Management Through Time Series Analysis

The purpose of this study is to investigate the application of time series analysis in the management of retail inventory, with a particular emphasis on the precision of forecasting as well as the applicability of ARIMA models and seasonal decomposition techniques. This study aimed to evaluate these models' effectiveness in improving the scientific and precise nature of inventory management. This was accomplished by using legitimate retail data. According to the findings, the ARIMA model is effective in managing data that is stable or does not exhibit seasonal patterns. On the other hand, the seasonal decomposition technique demonstrates strong forecasting accuracy in managing data that exhibits seasonal fluctuations. Not only does this research improve the theoretical implementation of time series analysis in the retail sector, but it also provides retailers with practical strategies for mitigating economic losses that are caused by inventory imbalance. When it comes to the management of retail inventory, the findings of the research are significant for improving both overall cost control and customer satisfaction.

Application of time series analysis in predicting postpartum depression: integrating data from the hospitalization period and early postpartum weeks.

This study aimed to explore the dynamic changes in postpartum depressive symptoms from the hospitalization period to 4-8 weeks postpartum using time series analysis techniques. By integrating depressive scores from the hospital stay and the early postpartum weeks, we sought to develop a predictive model to enhance early identification and intervention strategies for Postpartum Depression (PPD). A longitudinal design was employed, analyzing Edinburgh Postnatal Depression Scale (EPDS) scores from 1,287 postpartum women during hospitalization and at 4, 6, and 8 weeks postpartum. Descriptive statistics summarized demographic characteristics. Time Series Analysis using the Auto-Regressive Integrated Moving Average (ARIMA) model explored temporal trends and seasonal variations in EPDS scores. Correlation analysis examined the relationships between EPDS scores and demographic characteristics. Model validation was conducted using a separate dataset. EPDS scores significantly increased from the hospitalization period to 4-8 weeks postpartum (p < .001). The ARIMA model revealed seasonal and trend variations, with higher depressive scores in the winter months. The model's fit indices (AIC = 765.47; BIC = 774.58) indicated a good fit. The Moving Average (MA) coefficient was - 0.69 (p < .001), suggesting significant negative impacts from previous periods' errors. Monitoring postpartum depressive symptoms dynamically was crucial, particularly during the 4-8 weeks postpartum. The seasonal trend of higher depressive scores in winter underscored the need for tailored interventions. Further research using longitudinal and multi-center designs was warranted to validate and extend these findings. Our predictive model aimed to enhance early identification and intervention strategies, contributing to better maternal and infant health outcomes.

Assessing the impact of climate variability on wheat yield in Bloemfontein wheat farms through time series analysis

Climate change, characterized by long-term shifts in global or regional weather patterns, is a consequence of natural processes and human activities. These shifts encompass al-terations in temperature, precipitation, wind patterns, and other climatic variables, all of which exert direct influence on crop growth, development, and overall agricultural productivity. Comprehending the intricate relationship between climate change and crop production is par-amount for formulating strategies to counteract adverse effects and adapt to evolving conditions. This study focuses on assessing the impact of climate variability on wheat yield in Bloemfontein wheat farms through rigorous time series analysis. The research involved the application of various time series models, including SARIMA, ARIMA, Facebook Prophet, LSTM, VAR, and Multiple Linear Regression. The investigation began with forecasting temperature patterns using SARIMA and Facebook Prophet models. SARIMA outperformed Facebook Prophet in this context, as evidenced by lower RMSE and MSE metrics. Subsequently, the study delved into predicting rainfall and precipitation, employing ARIMA and LSTM models. In this case, LSTM demonstrated superior predictive capabilities. Finally, wheat production yield was analyzed using VAR and Multiple Linear Regression, with VAR yielding more accurate predictions. The findings of this study hold profound implications for policymakers, farmers, and stakeholders deeply invested in agriculture and food security. By shedding light on the repercussions of climate change on crop production through the application of time series analysis, this project aspires to contribute to developing sustainable agricultural practices, robust farming systems and proactive policies de-signed to mitigate the adverse effects of climate change on global food production.

Application of Time Series Analysis and Regression Algorithm in Assessing the Effectiveness of Monetary Policy in the Era of Digital Economy

Monetary policy is the series of actions to manage country's money supply and to attain economic growth. An objection to using interest rates in monetary policy is that the use of interest rates has the problem of time uncertainty. Therefore, this research introduces Conditional Autoregressive Value-at-Risk and Henry Gas Solubility Optimization (Caviar-HGSO) with Deep Long Short Term Memory (DeepLSTM) to monetary policy forecasting to demonstrate its effectiveness. Here, Caviar-HGSO is a hybrid of Caviar and HGSO used to tune DeepLSTM weights. The HGSO algorithm follows the wind state to balance exploration and exploitation in search space while avoiding local optimization. A Caviar model estimates the parameter over time by autoregressive process and determine a parameter by regression analysis. The advantages of these two optimization algorithms lead to better monetary policy forecasts. The experimental results show that Caviar-HGSO_DeepLSTM performs better regarding MAE, MSE, and RMSE, namely 0.438, 0.103, and 0.351.

Testing for periodicity at an unknown frequency under cyclic long memory, with applications to respiratory muscle training

AbstractA frequent problem in applied time series analysis is the identification of dominating periodic components. A particularly difficult task is to distinguish deterministic periodic signals from periodic long memory. In this paper, a family of test statistics based on Whittle’s Gaussian log-likelihood approximation is proposed. Asymptotic critical regions and bounds for the asymptotic power are derived. In cases where a deterministic periodic signal and periodic long memory share the same frequency, consistency and rates of type II error probabilities depend on the long-memory parameter. Simulations and an application to respiratory muscle training data illustrate the results.

Application of time series analysis to classify therapeutic breathing patterns

ObjectiveCompare various methods for measuring time series similarity in order to classify referenced therapeutic breathing patterns (BP) used in respiratory disorder rehabilitation. MethodsThis experimental study involved the collection of respiratory signals during specified breathing exercises conducted with healthy volunteers. The study employed a screening phase using a k-NN classifier and eight distance measurement methods, including Minkowski Distance, Dynamic Time Warping-DTW (including FastDTW and constrained-cDTW variations), Longest Common Subsequence-LCSS, Edit Distance on Real Sequences-EDR, Time Warp Edit Distance-TWEED, and Minimum Jump Costs-MJC. Two distinct approaches were employed for classifying therapeutic BP based on time series similarity: (1) using the k-Shape algorithm for clustering, and 2) integrating methods to represent therapeutic BP and classify test curves using the most relevant measurement methods obtained from the first approach. ResultsAmong the two tested approaches, the combination of the cDTW algorithm and Minkowski distance (p = 2), using the 1-NN classifier, achieved the highest scores in this study, closely matching the metrics obtained from visual inspection conducted by human evaluators. ConclusionThe use of combined classification methods in the analysis of flow curves referring to therapeutic breathing patterns improves the classification results, with metrics closely aligned with those obtained through visual evaluation conducted by individuals. SignificanceTime series analysis methods proved to be sensitive to classify respiratory flow curves equivalent to therapeutic breathing patterns used in respiratory disorder rehabilitation. This methodology can be used to monitor respiratory curves in new applications and implementation in devices for evaluating and treating the ventilatory pattern.

The wastewater treatment plant operation estimation through the use of the water quality index – the case study of WWTP-Montana, Bulgaria

Wastewater treatment plants (WWTPs) are designed to treat the used water by improving its quality, so it is no longer harmful to the environment when discharged. The long-term mandatory monitoring of wastewater produces large amounts of data. It can be converted to a unitless number – the Water Quality Index (WQI) and used to assess the wastewater quality by checking compliance with the set regulations. It has gained increasing popularity among decision-makers, wastewater professionals, and environmental agencies. Operation assessment of the WWTP-Montana for a period of 12 years (2011-2022) was performed using the Canadian Council of Ministers Water Quality Index (CCME WQI) calculation for the raw water (influent) and the treated water (effluent) of the WWTP and applying time series analysis of CCME WQI and water quality indicators – chemical oxygen demand (COD), biochemical oxygen demand after 5 days (BOD5), total nitrogen (TN), total phosphorus (TP) and total suspended solids (TSS) in the influent. For the entire period, the calculated CCME WQI for the treated waters shows the perfect score of 100 (excellent water quality). The calculated CCME WQI at the inlet, on the other hand, classifies the raw water’s quality as “poor” (64% of the CCME WQI values), “marginal” (32%) and “fair” (4%). The time series analysis reveal that higher water quality of the inlet wastewater is detected in summer (August) due to the lower concentrations of the five mandatory physicochemical indicators.

Applying Time Series Analysis to Study the Development Trend and Influencing Factors of Ethnic Folk Dance

Abstract In this paper, firstly, we propose to use time series analysis to observe the development and change process of social events, using the DF test and ADF test to eliminate the random autocorrelation in order to exclude the interference of white noise series. Secondly, the instantaneous frequency of non-smooth data is defined by analyzing the signal, and the constraints are modified from global constraints to local constraints. The Fourier-transformed eigenmode function is used to satisfy the four necessary conditions of completeness, orthogonality, localization, and adaptability. Finally, the development trend of ethnic folk dances and the change in communication dynamics are analyzed. The results show that the change of language on the spreading attitude of folk dance is 4.381, the change of custom on the spreading attitude is 5.943, the change of culture on the spreading attitude is 10.866, and the change of culture on the spreading attitude of folk dance is the largest, which shows that the development of folk dance away from the development of folk dance is not separated from the support of culture. The study will have a positive impact on passing on the distinctive art of ethnic folk dance and enriching Chinese traditional culture and art.

Copula Models of COVID-19 Mortality in Minnesota and Wisconsin

In this study, we assess COVID-19-related mortality in Minnesota and Wisconsin with the aim of demonstrating both the temporal dynamics and the magnitude of the pandemic’s influence from an actuarial risk standpoint. In the initial segment of this paper, we discuss the methodology successfully applied to describe associations in financial and engineering time series. By applying time series analysis, specifically the autoregressive integrated with moving average methods (ARIMA), to weekly mortality figures at the national or state level, we subsequently delve into a marginal distribution examination of ARIMA residuals, addressing any deviation from the standard normality assumption. Thereafter, copulas are utilized to architect joint distribution models across varied geographical domains. The objective of this research is to offer a robust statistical model that utilizes observed mortality datasets from neighboring states and nations to facilitate precise short-term mortality projections. In the subsequent section, our focus shifts to a detailed scrutiny of the statistical interdependencies manifesting between Minnesota and Wisconsin’s weekly COVID-19 mortality figures, adjusted for the time series structure. Leveraging open-source data made available by the CDC and pertinent U.S. state government entities, we apply the ARIMA methodology with subsequent residual distribution modeling. To establish dependence patterns between the states, pair copulas are employed to articulate the relationships between the ARIMA residuals, drawing from fully parametric models. We explore several classes of copulas, comprising both elliptic and Archimedean families. Emphasis is placed on copula model selection. Student t-copula with the marginals modeled by non-standard t-distribution is suggested for ARIMA residuals of Minnesota and Wisconsin COVID mortality as the model of choice based on information criteria and tail cumulation. The copula approach is suggested for the construction of short-term prediction intervals for COVID-19 mortality based on publicly available data.

Holocene vegetation dynamics of the Eastern Mediterranean region: Old controversies addressed by a new analysis

AbstractAimWe reconstruct vegetation changes since 12 ky in the Eastern Mediterranean to examine four features of the regional vegetation history that are controversial: the extent of non‐analogue vegetation assemblages in the transition from the Late Glacial to the early Holocene, the synchroneity of postglacial forest expansion, the geographical extent of temperate deciduous forest during the mid‐Holocene and the timing and trigger for the re‐establishment of drought‐tolerant vegetation during the late Holocene.LocationThe Eastern Mediterranean–Black Sea Caspian Corridor.TaxonVascular plants.MethodsWe reconstruct vegetation changes for 122 fossil pollen records using a method that accounts for within‐biome variability in pollen taxon abundance to determine the biome with which a sample has greatest affinity. Per‐biome affinity threshold values were used to identify samples that do not belong to any modern biome. We apply time series analysis and mapping to examine space and time changes.ResultsSites with non‐analogue vegetation were most common between 11.5 and 9.5 ky and mostly in the Carpathians. The transition from open vegetation to forest occurred at 10.64 ± 0.65 ky across the whole region. Temperate deciduous forest was not more extensive at 6 ky; maximum expansion occurred between 5.5 and 5 ky. Expansion of forest occurred between c. 4 and 2.8 k, followed by an abrupt decrease and a subsequent recovery. This pattern is not consistent with a systematic decline of forest towards more drought‐tolerant vegetation in the late Holocene but is consistent with centennial‐scale speleothem patterns linked to variations in moisture availability.Main ConclusionsWe show the occurrence of non‐analogue vegetation types peaked during early Holocene, forest expansion was synchronous across the region and there was an expansion of moisture‐demanding temperate trees around 5.5 to 5 ky. There is no signal of a continuous late Holocene aridification, but changes in forest cover appear to reflect climatic rather than anthropogenic influences.

Forecasting multicycle hollow fiber ultrafiltration fouling using time series analysis

This study proposes the application of time series analysis and exponential smoothing (ETS) to predict multicycle membrane fouling with high accuracy and great interpretability. First, lab-scale filtration tests were performed with foulant surrogates to assess the potential of ETS models in predicting multicycle membrane fouling. Two models, ETS (A,A,A) and ETS (A,A,M), were found to be most suitable for describing the variation of transmembrane pressure. The ETS (A,A,A) model showed a slight edge over the ETS (A,A,M) model when reversible fouling was dominant. In contrast, the ETS (A,A,M) performed better when irreversible fouling was prevalent. Then, the effectiveness of ETS models in multicycle forecasting during the filtration of natural water resources was evaluated. Residual mean squared errors between 0.02 bar and 0.15 bar were obtained for horizons going from 2 up to 10 filtration cycles. Finally, ETS models were applied to predict the variation in permeability of drinking water treatment plant membrane skid. A three-month dataset was utilized to predict the next 41 days of permeability variation, yielding an average error of 3.2 %. Along with that, a web application designed to assist membrane users in utilizing ETS was developed and presented at the end. The web application can be accessed through the following link: https://fouling-ets.shinyapps.io/shiny-ets-forecasting/.

Applications of time series analysis in epidemiology: Literature review and our experience during COVID-19 pandemic

Time series analysis is a valuable tool in epidemiology that complements the classical epidemiological models in two different ways: Prediction and forecast. Prediction is related to explaining past and current data based on various internal and external influences that may or may not have a causative role. Forecasting is an exploration of the possible future values based on the predictive ability of the model and hypothesized future values of the external and/or internal influences. The time series analysis approach has the advantage of being easier to use (in the cases of more straightforward and linear models such as Auto-Regressive Integrated Moving Average). Still, it is limited in forecasting time, unlike the classical models such as Susceptible-Exposed-Infectious-Removed. Its applicability in forecasting comes from its better accuracy for short-term prediction. In its basic form, it does not assume much theoretical knowledge of the mechanisms of spreading and mutating pathogens or the reaction of people and regulatory structures (governments, companies, etc. ). Instead, it estimates from the data directly. Its predictive ability allows testing hypotheses for different factors that positively or negatively contribute to the pandemic spread; be it school closures, emerging variants, etc. It can be used in mortality or hospital risk estimation from new cases, seroprevalence studies, assessing properties of emerging variants, and estimating excess mortality and its relationship with a pandemic.

Advanced application of time series analysis in complex karst aquifers: A case study of the Unica springs (SW Slovenia)

Karst aquifers have very heterogeneous structure and complex hydrodynamics through conduits, fractures and matrix, therefore characteristics of groundwater flow and solute transport differ from those in intergranular and fissured aquifers. Appropriately adapted techniques of hydrogeological research are required for understanding of their functioning. This paper presents an advanced application of time series analysis in a binary karst aquifer known for its complex hydrodynamics and mixing of water from various sources of recharge. A classical approach with high temporal data resolution was extended to a spatial domain with simultaneous monitoring of precipitation, sinking streams, water flow in cave systems, and springs. The main objectives of this study were to define and compare the flow characteristics and storage capacity of selected springs and their catchments, and to determine the influence of different types of recharge (autogenic versus allogenic). The time series of recharge were analysed as input functions in the correlation and spectral analysis. The results undoubtedly show differences in water transfer through the system, storage capacity, and recharge characteristics. However, they also highlight the limitations of using cross-correlation functions to distinguish the influence of autogenic and allogenic recharge. Due to their interference, the interpretation of the results in complex karst systems can be ambiguous, therefore a method of partial cross-correlation analysis was additionally used. Although it has been used before in karst aquifers to spatially characterise groundwater circulation and autogenic recharge, in this study it was used for the first time to investigate the mutual influences of allogenic and autogenic recharge. The results confirmed that this approach provides additional insight into the functioning of binary karst aquifers.

Senior High School Student Enrollment Forecasting Model: An Application of Time Series Analysis

Senior High School Student Enrollment Forecasting Model: An Application of Time Series Analysis

A jolt to the system: Quantifying disaster impact and return to routine using citizen-generated calls for service.

This study compares the effect of two different types of incidents on the number of citizen-generated 9-1-1 dispatch center calls and if changes in the call numbers represent a measurable break in the expected rhythm of 9-1-1 calls. Using time series analysis, changes in the normal rhythm of calls for service (CFS) demonstrate that CFS is a good indicator of a disaster event. CFS data may potentially illustrate one aspect of measuring the degree of disaster for an event. This study establishes the value of applying time series analysis to secondary data within the framework of social routine to determine the magnitude disaster impact (or jolt) to a system. The same methodology may also be applied to examine the process of reestablishing system routine or rhythms indicating system recovery as defined as restabilization.

Estimation of Height Changes of Continuous GNSS Stations in the Eastern Anatolia Region during the Seasonal Variation

Estimating the height component of Global Navigation Satellite System (GNSS) stations is widely known to be more challenging than estimating the horizontal position. In this study, we utilized height time series data from 37 continuous GNSS stations that were part of the Turkish RTK CORS Network called TUSAGA-Active (Turkish National Permanent GNSS Network Active). The data covered the period from 2014 to 2019, and the selection of stations focused on the Eastern Anatolia region of Turkey due to its topographic characteristics and the pronounced influence of seasonal changes, which facilitated the interpretation of the effects on the height component. The daily coordinates of the GNSS stations were derived using the GAMIT/GLOBK software solution. We identified statistically significant trends, periodic variations, and stochastic components associated with the stations by applying time series analysis to these daily coordinate values. As a result, the vertical velocities of the GNSS stations were determined, along with their corresponding standard deviations. Furthermore, examining the height components of the continuous GNSS stations revealed seasonal effects. We aimed to investigate the potential relationship between these height components and meteorological parameters. The study provides evidence of the interconnectedness between the height components of continuous GNSS stations and various meteorological parameters. Simple linear regression analysis and ARMA time series modeling were utilized to establish this relationship.

Precipitation variations in the central Vietnam to forecast using Holt-Winters Seasonal Additive Forecasting method for 1990 to 2019 trend

Precipitation played a vital role in the landslide events, water cycle, irrigation management, agriculture yield. The major factor is the extreme weather such as temperature played a vital role in it. This study applied climatological data (30 years of span) of two different geolocation at Vietnam, and applied time series analysis, Holt-Winters Seasonal Additive Forecasting model. The highest Precipitation i.e. 260.83 and 112.96 mm in the month of September and November for Danang and Pleiku, respectively; and total sum of 66550.51 and 34118.38 mm over the period for Danang and Pleiku, respectively. Danang is with higher Precipitation in lesser number of events, and Pleiku is with lower Precipitation though more number of events. Holt-Winters Seasonal Additive Forecasting method revealed the potential forecasting method for the different statistical characteristics data set. Lag plot showed the higher positive as well as the negative lap quartile. Residual error plot present to support the applied model feasibility. Weakness of the study and the future objective of the study drafted.

When the Personal Becomes Political: Unpacking the Dynamics of Sexual Violence and Gender Justice Discourses Across Four Social Media Platforms

We propose a three-pronged framework to study discourses surrounding social media activism initiated by networked counterpublics: personalized expressions that share stories and support, demands for changes that address systematic problems, and contentions between various actors and perspectives. Situating our analysis in discourses related to sexual violence and gender justice activism on Twitter, Facebook, Instagram, and Reddit, we use supervised machine learning to quantify three discourses—networked acknowledgment, calls to action, and feminism contention—and apply time series analysis to model their interrelations. Results show that networked acknowledgment stimulated both calls to action and feminism contention and that calls to action predicted feminism contention across all platforms. These discourses were more sensitive to real-world events on Twitter and Facebook, but more ephemeral and cyclical on Instagram and more persistent and coupled on Reddit. Our findings speak to the opportunities and challenges in social media activism and underscore cross-platform similarities and differences.