Application Of Time Series Analysis Research Articles

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.

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.

Application of Time Series Analysis in Modelling an Integrated Moving Average for Predicting Caesarean Sections Births

Purpose: Periodic variations in caesarean sections incidences at the University of Cape Coast Hospital is currently unclear and the model that can best represent its movement is unknown. This study sought to model and predict the monthly incidence of caesarean sections at the hospital.
 Methods: The study employed a monthly periodicity of 115 time-series data sourced from District health information management systems Two on caesarean section births at the University of Cape Coast Hospital over a 9-year period. The auto regressive integrated moving average models of the classical Box-Jenkins methods of Time Series Analysis were used to analyse the data. Analysis was performed in EViews 12 and R (version 4.0.4)
 Findings: There were six non-seasonal tentative candidate models for the hospital. The best fit model had an autoregressive integrated moving average with a first-order autoregressive and a second-order moving average. The findings revealed an overall rising trend in the incidence of caesarean section rates in the hospital over the study period with an average of 30.54% per month. This is expected to increase to over 40% per month over the next five-year period of August 2021 to July 2026 according to projections.
 Conclusion: The best fit model can be used to forecast the expected number of caesarean section births.
 Recommendations: Clinicians should be trained to improve their skills in the use of instruments in deliveries as well as in the safe conduct of vaginal breech deliveries.

Modelling and Forecast of Air Pollution Concentrations during COVID Pandemic Emergency with ARIMA Techniques: the Case Study of Two Italian Cities

An efficient and punctual monitoring of air pollutants is very useful to evaluate and prevent possible threats to human beings’ health. Especially in areas where such pollutants are highly concentrated, an accurate collection of data could suggest mitigation actions to be implemented. Moreover, a well-performed data collection could also permit the forecast of future scenarios, in relation to the seasonality of the phenomenon. With a particular focus on COVID pandemic period, several literature works demonstrated a decreasing of pollutant concentrations in air of urban areas, mainly for NOx, while CO and PM10, on the opposite, has been observed to remain still, mainly because of the intensive usage of heating systems by the people forced to stay home (on specific regions). With the present contribution the authors here present an application of Time Series analysis (TSA) approach to pollutants concentration data of two Italian cities during first lockdown (9 march – 18 may 2020), demonstrating the possibility to predict pollutants concentration over time.

Application of Time Series Analysis for Better Decision Making in Business

The research study discusses the relevance of “time series analysis” applications that will help an organisation to understand business growth. The present study affects an individual’s knowledge that ensures the research's relevance. The present study focuses on several business metrics that have used to discuss the effectiveness of time series. The researcher has also focused on an appropriate methodology that increases knowledge of the research study by adopting a research design, type, approach, and others. The study chooses a secondary qualitative data analysis method that affected the entire research study. Accessing through time series of analytical approach processes manage to process through an effective decision-making range is essential to provide effective decisions and its courses through the systematic process of review. The benefits of time series analysis personal concepts of series resources and its importance through the decision-making range are essential. A systematic approach in businesses access through proposed factors in business beneficial range and attribution of specific concepts helps to organize this aspect. A systematic source of decision-making range provides optimization of proposed business provides concepts of business making process. Accessing through the kinetic model summary has been provided in terms of beneficial business courses.

A data-driven high spatial resolution model of biomass accumulation and crop yield: Application to a fragmented desert-oasis agroecosystem

Information on crop yield is important for food security, in particular under the conditions of climate change and growing population worldwide. We developed a new fully distributed, high spatial resolution, model of biomass accumulation and crop yield applicable to a highly heterogeneous desert-oasis agroecosystem. The bulk of required input data is obtained by retrieving pixel-wise biogeophysical variables from a suite of very diverse satellite data. Both temperature and water stress conditions at field-scale are given full consideration, while the model was designed to strike a balance between model applicability and satisfactory characterization of the heterogeneous desert-oasis system to estimate field-scale yield. The development of this model relies on three main innovations. First, the start and end of the growing season were estimated for each pixel by calibrating the high spatial and temporal resolution observations of Normalized Difference Vegetation Index (NDVI) by Sentinal-2 (S2) MSI (Multi-Spectral Instrument) against limited local phenological information. Second, to monitor crop water stress, account taken of irrigation, a process-based water and energy balance model was applied to estimate the actual evapotranspiration (ET). This requires knowledge of soil water availability, which is characterized by downscaling the ASCAT (Advanced SCATterrometer) soil moisture data product. To capture the dominant features of the eco-hydrological conditions in the desert and oasis agroecosystem, ET was further downscaled from the 1 km resolution. Third, likewise the water stress indicator, the air temperature stress indicator was mapped after characterizing the thermal contrast and heterogeneity of the desert-oasis system, by generating time series of air temperature at 1 km spatial resolution using the MODIS (Moderate Resolution Imaging Spectroradiometer) Land Surface Temperature (LST) data product. In the temporal dimension, gaps were mitigated by applying time series analysis techniques to reconstruct cloud-free time series of LST, NDVI, fAPAR and albedo. These innovations add up to a high resolution characterization of crop response to the geospatial variability of weather and climate forcing in the desert-oasis agroecosystem. The model was applied to the dominant crops, i.e., spring wheat, maize, sunflower, and melon, in the oases of the Shiyang River Basin (northwestern China) characterized by a rather fragmented land use. The high resolution of pixel-wise ecohydrological parameters, i.e., crop phenology, temperature stress and water stress factors successfully reflect differences of crops with different phenology and location in the oases. The relative errors for wheat and maize yields compared to the census data are less than 5% at district level. At the county level, the relative errors of wheat yields of Liangzhou, Minqin, Gulang, Jinchuan, and Yongchang equal to 0.87%, 24.2%, 9.7%, 12.5%, and 7.2%. For maize, the dominant crop, the error on estimated yields was less than 5%, except in Gulang. The relative error on estimated yield for sunflower was less than 10% compared to agricultural census data. The relative error on estimated melon yield was 16%. This performance highlights the applicability of the model to estimate field-scale yields in agroecosystems characterized by fragmented land use.

Geographic and socioeconomic differences in heat-related mortality among the Dutch population: a time series analysis

ObjectivesThis study was conducted to examine modification in heat-related mortality in the Netherlands by sociodemographic and geographical factors including socioeconomic position and population density (PD).DesignThis observational study applied time series...

Electricity Price Instability over Time: Time Series Analysis and Forecasting

Competition in electricity markets leads to volatile conditions which cause persistent price fluctuations over time. This study explores the problem of electricity pricing fluctuations in the DE-LU bidding zone from October 2018 to March 2022 by applying time series analysis. The determinants of electricity price fluctuations are broken down into three groups: exogenous prices (gas, coal and CO2 prices), internal (consumption and generation) and external (net import between neighboring bidding zones) electricity flows. Based on the SARIMAX model, we tried to combine all these factors to forecast electricity prices in the single bidding zone. It was found that the SARIMAX (1, 1, 2) × (3, 1, 0, 7) model with exogenous prices, internal and external electricity flows, which has the lowest AIC and MAPE values, is the best-fitted model for the DE-LU bidding zone. Anonymous trading and unpredictable individual bidding strategies lead to persistent price volatility, which causes electricity prices to deviate from fundamental trends. To reveal the risk factors, the SARIMAX model of electricity prices needs to be supplemented with a GARCH model of the residual returns. For forecasting electricity price residual volatility in the DE-LU bidding zone, the SARIMAX model with exogenous prices, internal and external electricity flows must be accompanied with the GARCH (7, 0) model.