Time Series Analysis Methods Research Articles

Strain variation and anomalous climate synergistically influence cholera pandemics.

Explanations for the genesis and propagation of cholera pandemics since 1817 have remained elusive. Evolutionary pathogen change is presumed to have been a dominant factor behind the 7th "El Tor" pandemic, but little is known to support this hypothesis for preceding pandemics. The role of anomalous climate in facilitating strain replacements has never been assessed. The question is of relevance to guide the understanding of infectious disease emergence today and in the context of climate change. We investigate the roles of climate and putative strain variation for the 6th cholera pandemic (1899-1923) using newly assembled historical records for climate variables and cholera deaths in provinces of former British India. We compare this historical pandemic with the 7th (El Tor) one and with the temporary emergence of the O139 strain in Bangladesh and globally. With statistical methods for nonlinear time series analysis, we examine the regional synchrony of outbreaks and associations of the disease with regional temperature and rainfall, and with the El Niño Southern Oscillation (ENSO). To establish future expectations and evaluate climate anomalies accompanying historical strain replacements, climate projections are generated with multi-model climate simulations for different 50-year periods. The 6th cholera pandemic featured the striking synchronisation of cholera outbreaks over Bengal during the El Niño event of 1904-07, following the invasion of the Bombay Presidency with a delay of a few years. Accompanying anomalous weather conditions are similar to those related to ENSO during strain replacements and pandemic expansions into Africa and South America in the late 20th century. Rainfall anomalies of 1904-05 at the beginning of the large cholera anomaly fall in the 99th percentile of simulated changes for the regional climate. Evolutionary pathogen change can act synergistically with climatic conditions in the emergence and propagation of cholera strains. Increased climate variability and extremes under global warming provide windows of opportunity for emerging pathogens.

Regulatory pathways to green energy transition for sustainable environment: The fostering role of human rights, banking sector development, economic complexity, and economic freedom

Green energy transition directly contributes to Sustainable Development Goals 7 and 13, which are among the 17 development goals determined by the United Nations. However, what are the determinants of this green energy transition? Researchers' answers to this question will assist in formulating policy prescriptions that will enable concrete steps toward achieving these goals. This study investigates how the development of the banking sector, human rights, economic complexity, and economic freedom affect the green energy transition in China, which is the biggest emitter of greenhouse gases globally but is also one of the leading countries in renewable energy production. The study covers the years 1995–2022. Advanced time series analysis methods were employed, and robust results were reported. According to the findings, Economic Freedom, Human Rights, and Economic Complexity have increased the transition to green energy in China. No effect of banking sector development was found. The country should improve economic freedom and human rights and increase knowledge/innovation-based production to accelerate China's transition to green energy and contribute to environmental sustainability.

Mathematical modeling and seasonal solar radiation variability in Nigeria’s geo-political zones: A recurrence and multifractal analysis

This article delves into the seasonal variation of solar radiation patterns across Nigeria’s four geo-political zones, exploring their complex, scale-dependent behaviors. By employing chaotic quantifiers, the study characterizes irregular and self-similar patterns, enhancing our understanding of solar radiation variability and heterogeneity. The research uniquely fits meteorological parameters onto a global solar radiation model and focuses on the underexplored nonlinear aspects within tropical regions, specifically in the Nigerian context. Utilizing daily data from ERA INTERIM satellite archives for representative stations, the study employs nonlinear time series analysis methods like recurrence plots, recurrence qualitative analysis, and multifractal spectral analysis to comprehensively explore the unpredictable behaviors observed. The quantifier spectrums play a key role in revealing intricate scaling behaviors and correlation structures among environmental variables, shedding light on patterns often concealed by conventional statistical methods. For instance, we found that solar radiation variability in the northern region increases more significantly during the dry season compared to the wet season, unlike in the southern region. Additionally, the multifractal spectral analysis revealed a higher degree of complexity in solar radiation patterns during transition periods between seasons. The findings reveal a low recurrence quantitative analysis, long right tail, and truncations at both ends of the spectrum. This suggests instability in solar radiation across different seasons and locations. Nonetheless, the results also demonstrate that solar radiation is consistently available throughout the year, which is typical of tropical regions.

Robust Methods for Assessing the Characteristics of Locomotor Activity Based on Markerless Video Capture Data

Introduction. Analysis of locomotor activity is essential in a number of biomedical and pharmacological research designs, as well as environmental monitoring. The movement trajectories of biological objects can be represented by time series exhibiting a complex multicomponent structure and non-stationary dynamics, thus limiting the effectiveness of conventional correlation and spectral time series analysis methods. Recordings obtained using markerless technologies are typically characterized by enhanced noise levels, including both instrumental noise and anomalous errors associated with false estimates of the location of the points of interest, as well as gaps in the trajectories, promoting an urgent need in the development of robust methods to assess the characteristics of locomotor activity.Aim. Development of robust methods for assessing the characteristics of locomotor activity capable of efficient processing of noisy recordings obtained by markerless video-based motion capture systems.Materials and methods. In order to assess the characteristics of locomotor activity, the relative movements of body parts of laboratory animals were analyzed using the stability metrics of the mutual dynamics of their trajectories, their relative delays, as well as the relative duration of the recording fragments when relatively stable mutual dynamics could be observed. The local maxima of the cross-correlation function of two body fragments, the minima of the standard deviation of the difference between their Hilbert phases, as well as their relative delays, were used as the metrics of mutual dynamics.Results. The considered phase metrics were shown to explicitly reflect changes in locomotor activity, while the assessment of time delays using phase metric was shown to be prone to periodic error. The above limitation could be largely overcome using the correlation metrics, assuming that phase and correlation metrics could be combined.Conclusion. The proposed robust methods provide stable estimates of the characteristics of locomotor activity based on markerless video capture recordings, altogether increasing the efficiency of diagnostic procedures and assessment of the therapeutic effect during rehabilitation.

Nonlinear Characteristics of Radio Variability in Quasar 3C 446

Quasars are characterized by violent and large amplitude variability in all the observation wavelengths, and the analysis of optical variability is useful for developing theoretical models. Long-term optical variability data of quasar 3C 446 were collected from the University of Michigan Radio Astronomy Observatory database in the 4.8, 8.0, and 14.5 GHz radio bands from 1976 to 2012. Due to the complexity of the variability data, it's hard to study by the linear time series analysis methods. For learning more about non-linear characteristics of the temporal evolution of quasar variability, the Ensemble Empirical Mode Decomposition and nonlinear analysis are used to analyse chaotic dynamics, fractal properties, and periodicity. This paper focuses on whether there is a significant difference between the periodic and non-linear properties of the quasar variability before and after the removal of the periodic or chaotic components. It turns out that the variability of quasar 3C 446 in the radio bands consists of periodic, trend, and chaotic components, and the periodic and trend components are dominant. The periods of the variability after removing the chaotic and trend components are exactly the same as the periods of the original variability data, but the chaotic and fractal characteristics of the two are significantly different. The saturated correlation dimension indicates that the reconstruction of the dynamical system requires more independent parameters than the original optical variables after the removal of the periodic and trend components. The Kolmogorov entropy indicates that the loss of information is greater for the former than the latter, and the system is more chaotic and more complex. The Hurst value indicates that the self-similarity and long-range correlation are slightly stronger for the latter than the former.

Power Peak Load Forecasting Based on Deep Time Series Analysis Method

Power Peak Load Forecasting Based on Deep Time Series Analysis Method

FORECASTING THE VALUE OF IRON ORE RAW MATERIALS ON THE BASIS OF STATISTICAL TIME SERIES ANALYSIS

Objective. The objective of the present article is the development of evaluation of the effectiveness of forecasting the time series of cost indicators as stochastic using known methods. Methods. The following methods and techniques of cognition were used in the research process: theoretical generalization and comparison, analysis and synthesis, induction and deduction, generalization and systematization, statistical methods of time series analysis. Results. The article presents the stages of forming a statistical analysis of a time series. It has been established that in modern conditions of evaluating the effectiveness of economic research, a more thorough analysis of the dependence of value indicators on time is necessary. Their mathematical models are usually used to describe the behavior of physical objects. If a model based on physical laws can be obtained, such a model would be deterministic. At the same time, in practice, even such a model is not completely deterministic, since a number of unaccounted factors may participate in it. For such objects, it is not possible to offer a deterministic model that allows accurate calculation of the future behavior of the object. Nevertheless, it is possible to propose a model that allows you to calculate the probability that some future value will lie in a certain interval. Such a model is called stochastic. Time series models of commodity prices in the time domain are actually stochastic. An important class of stochastic models for describing time series are stationary models. They are based on the assumption that the process remains in equilibrium with respect to a constant average level, which is confirmed by studying the time series of the cost of goods in the time domain.Mathematical models of stochastic time series were built based on the study of the real dependence of indicators on time. In practical terms, this will improve the economic performance of the enterprise. For practical implementation, a stochastic time series of the cost indicator was constructed; an economic-mathematical model for the value indicator based on a time series was formed for the purpose of forecasting. The quality of the forecast is determined not only by the forecast error, but also by the number of parameters included in the model of the forecasting function. Analysis of the data shows that the smallest forecast error occurs for the analytical trend function. Along with this, the trend function has six parameters. If we take into account the number of parameters, then the best method will be the moving average, which has an error variance of 54 with one parameter.

Prediction of Energy Consumption on Example of Heterogenic Commercial Buildings

The management of large enterprises influences their efficiency and profitability. One of the important aspects is the appropriate management of electricity consumption used for production and daily operation. The problem becomes more complicated when you need to manage not one but a large complex of buildings with heterogeneous purposes. In the paper, we examine real-time series data of electrical energy consumption in a complex of heterogeneous buildings, including offices and warehouses, using time series analysis methods such as the Holt–Winters model and ARIMA/SARIMA model, and neural networks (Deep Neural Network, Recurrent Neural Network, and Long Short-Term Memory). Experimental research was performed on a dataset obtained from an energy consumption meter placed in the building complex, built in different periods, and equipped with a variety of automation devices. The data were collected over a period of four years 2018–2021 in the form of time series. Results show that classic models are good at predicting energy consumption in the mentioned type of buildings. The ARIMA model gave the best results—for buildings characterized by seasonality and trends the forecasts were almost perfect with actual values.

The Effect of Interest and Inflation Rates on Stock Returns: Quantile Regression Analysis for Türkiye

According to financial theory, there is an inverse relationship between interest rate and stock returns and prices. In the framework of the Liquidity Preference Theory, the rise in interest rates causes the money to move towards higher interest rates, so demand for stock market decreases and returns fall. The behaviour of stock prices on interest rates and inflation has been investigated by various time series analysis methods. However, in this study, the subject is handled in a different approach by using the Quantile Regression (QR) method. In the study, nominal interest rate is decomposed into real interest rate and inflation components and effects on stock prices are examined for the period January 2013 - July 2023 separately for services, financial, industrial and technology sector indices. When it comes to the impact of nominal and real interest rates on returns in QR estimates, it is seen that the service and industrial sectors differ in terms of both tail and sign. The similarities between Least Squares estimates and QR estimates are striking. However, the effect of volatility in variables on the differentiation in relationships is also an issue that should be emphasized.

Feasibility of anomalous event detection based on Mobile Spatial Statistics: A study of six cases in Japan

One of the primary goals of disaster risk management is to minimize the loss of life in the event of a disaster. To reduce the number of victims in disasters is important to know the amount of exposed population to a hazard. Similarly, population distribution in real-time is required to estimate post-disaster needs and resources for disaster relief. In this study, we explore the use of Mobile Spatial Statistics (MSS) as the basis to acquire offline and online population data and detect anomalies that may relate to a disaster. For instance, mobile spatial data could identify sudden changes in the number of people indicating an anomalous event or change in population dynamics. Here we use the Matrix Profile (MP) method in time-series analysis to test the feasibility of detecting anomalies in the MSS data. We selected mass gatherings and disaster events with rapid and slow onset and small and large scales. We discuss the applicability of this method for real-time anomaly detection of spatial population distribution within the context of disaster risk management. Our results show that the applicability of the MP method in anomaly detection is sensitive to the size of the areas used during monitoring and analysis, the subsequence length and the threshold applied.

Optimizing User Experience: A Data-Driven Approach to Enhancing B2B Invoicing Efficiency across Multiple Platforms PT Pakar Digital Global

This study is a deep dive into applying data-driven decision-making strategies to enhance user experience and efficiency in B2B invoicing across multiple platforms at Paper.id. Given the fintech industry’s rapid evolution, maintaining a competitive edge requires constant innovation, particularly in user activation and retention within the first month of usage. This research employs a comprehensive data analysis framework, integrating Time Series Analysis, Descriptive Analysis, and Correlation Analysis methods to extract actionable insights from user data stored in MySQL databases. The study also incorporates strategic planning tools like SWOT and TOWS analysis to identify and implement UI/UX enhancements using the Agile Scrum methodology. The effectiveness of these interventions is evaluated through a robust post-analysis, comparing metrics before and after the implementation across mobile and desktop platforms. Results demonstrate a significant improvement in user activation and retention rates on mobile platforms, validating the efficacy of the applied strategies. This paper contributes valuable insights into optimizing user experience in a dynamic market, emphasizing a structured, iterative approach to achieving sustained user engagement and business growth.

Cryptocurrency, Gold, and Stock Exchange Market Performance Correlation: Empirical Evidence

This paper examines the correlation between three prospective investing options: the Bitcoin cryptocurrency price, gold, and the Dow Jones stock index. The main research question is whether there is a causal effect of gold and the DWJ on Bitcoin and how this effect varies on time. The study begins with a background analysis that explains the definitions and operation of cryptocurrencies, followed by a brief overview of gold and its derivatives. In addition, a historical review of stock markets is provided, with a focus on the Dow Jones index. Then, a literature review follows. Daily data from three separate periods are used, each spanning four years. The first period, running from October 2014 to September 2018, provides an overview of the introduction of official cryptocurrency price data. The second period, running from Oct 2018 to Sept 2022, captures more recent trends preceding COVID-19. The third period, from January 2020 to December 2023, is the whole COVID-19 period with the initiation, embedded, and terminal phases. Classical inductive statistical methods (descriptive, correlations, multiple linear regression) as well as time series analysis methods (autocorrelation, cross-correlation, Granger causality tests, and ARIMA modeling) are used to analyze the data. Rigorous testing for autocorrelation, multicollinearity, and homoskedasticity is performed on the estimated models. The results show a correlation of Bitcoin with gold and the DWJ. This correlation varies over time, as in the first period the correlation mainly concerns the DWJ and in the second it mainly concerns gold. By using ARIMA models, it was possible to make a forecast in a time horizon of a few days. In addition, the structure of the forecasting mechanism of gold and DWJ on Bitcoin seems to have changed during the COVID-19 crisis. The findings suggest that future research should encompass a broader dataset, facilitating comprehensive comparisons and enhancing the reliability of the conclusions drawn.

Search for anomalies in Stromboli's pre-paroxysm activity through an automatic hybrid method of time series analysis

Stromboli (Italy) is an open-vent volcano with persistent explosive activity producing up to five hundred mild explosions per day. Fluctuations in explosion intensity, varying even by orders of magnitude in terms of emitted volume and their subsequent impact on the surrounding regions, sometimes occur abruptly. Consequently, identifying precursors of larger eruptive activities, particularly for more intense (paroxysmal) explosions, is challenging. In order to search for anomalies in the pre-paroxysm activity related to the summer 2019 eruption, we applied a hybrid method to the automatic analysis of geophysical and geochemical time series. This approach is based on the combination of two methods: 1. the Empirical Mode Decomposition (EMD) and 2. the Support Vector Regression (SVR). The aggregation of these two methods allowed us to identify anomalies in the patterns of the geophysical and geochemical parameters measured on Stromboli in a ten-month period including the July–August 2019 eruption. The results of this study are encouraging for an improvement of the monitoring systems and for volcano early warning applications.

Comparison of the Workday and Non-Workday Carbon Emission Reduction Benefits of Bikeshare as a Feeder Mode of Metro Stations

Bikeshare, as a convenient transport mode, can address the first- and last-mile travel needs of metro trips while generating many environmental benefits, such as reducing the use of environmentally unfriendly transport modes and lowering the carbon emissions of the urban transportation system. This paper takes bikeshare as a feeder mode of metro stations (BS-FMMS) as the research object and compares the spatial and temporal differences in the carbon emission reduction benefits of BS-FMMS on workdays and non-workdays by using the framework of BS-FMMS carbon reduction benefit analysis and the methods of time-series analysis, spatial aggregation analysis, and box plot analysis. The results show that the carbon emission reduction benefit of bikeshare has obvious morning and evening peaks on workdays, while it tends to be stable without obvious peaks during the day on non-workdays. From the perspective of spatial distribution, the carbon emission reduction benefits of BS-FMMS are more significant in the metro station areas in the south of Baoan district, the west of Nanshan district, the central of Longhua district, and the south of Futian district in Shenzhen city, and the metro stations where the carbon emission reduction benefits of the non-workday are greater than those of the workday are mainly concentrated in Nanshan district, Futian district, and Luohu district. There is a significant positive correlation between BS-FMMS ridership and carbon emission reduction. These findings can provide clear policy implications for the decarbonization development of urban transportation systems.

Analisis rasio keuangan untuk menilai kinerja keuangan pada PT Unilever Indonesia Tbk Tahun 2022-2023

Financial performance of PT. Unilever Indonesia Tbk is evaluated using financial statement analysis. The purpose of this study is to evaluate the financial performance of PT. Unilever Indonesia Tbk from 2022 to 2023 through financial statement analysis. The time series analysis method is used to understand changes in a company's financial performance over time. It involves the use of various financial ratios including liquidity, solvency, activity, and profitability ratios. This research uses a quantitative descriptive approach, which means that the data collected is quantitative data from the annual financial statements of PT. Unilever Indonesia Tbk (secondary data) for 2022 and 2023. The analysis results show the company's liquidity ratio is below industry standards, indicating challenges in paying off short-term obligations. Although the cash ratio will increase in 2023, the value is still not ideal. In terms of solvency, the ratio of debt to asset and debt to equity indicates a high dependence on debt. However, the company performed well in profitability, with profit margins, return on assets (ROA), and return on equity (ROE) above the industry average, reflecting effectiveness in generating profits and managing assets and capital. The inventory turnover ratio is below industry standard, but total asset turnover shows good performance, indicating effective asset management to generate sales.

Comparative Analysis of Univariate SARIMA and Multivariate VAR Models for Time Series Forecasting: A Case Study of Climate Variables in Ninahvah City, Iraq

This study involves a comparison between the application of the univariate SARIMA model and the utilization of VAR methods (vector autoregressive models) for multivariate time series analysis. The analysis is conducted using three-time series variables derived from data representing the monthly average of Humidity (H), Rainfall (R), and Temperature (T) in Ninahvah City, Iraq. Both univariate and multivariate time series approaches are employed to model these series. The paper also outlines the implementation of vector autoregressive, structural vector autoregressive, and structural vector error correction models using the 'vars' package. Additionally, it provides functions for diagnostic testing, estimation of constrained models, prediction, causality analysis, impulse response analysis, and forecast error variance decomposition. Furthermore, it introduces three fundamental functions, VAR, SVAR, and SVEC, for estimating these models. The comparison between the methods is based on evaluating the mean error produced by each approach. The findings of the study indicate that univariate linear stationary methods outperform multivariate models. The analysis of the data was carried out using the R software platform. The primary objective is to assess the performance of univariate and multivariate time series models in handling the given data. The research gap lies in the need for a comparative evaluation of SARIMA and VAR methods for time series analysis in the context of monthly environmental variables. These models were chosen due to their effectiveness in capturing temporal dependencies and interactions among multiple variables in time series data, providing a comprehensive analysis of climatic patterns in Ninahvah City, Iraq. The study aims to address the research gap by comparing these models and justifying their selection based on their capabilities to analyze the specified time series data.

Aplikasi Metode Double Exponential Smoothing Holt Dengan Optimasi Golden Section Untuk Peramalan Nilai Ekspor Provinsi Kalimantan Timur

Exponential smoothing is a method of time series analysis used to forecast the future. The choice of forecasting method takes into account data patterns, such as Double Exponential Smoothing (DES) Holt is used on data that has a trend pattern, and Triple Exponential Smoothing (TES) Holt-Winters is used on trend and seasonal data. The aim of this research is to obtain the best forecasting method by optimizing the golden section on the export value of East Kalimantan Province from 2015 to 2022 and to obtain the results of forecasting the export value of East Kalimantan Province for the next 3 months using the best method by optimizing the golden section. The research results show that the parameter value using golden section optimization is for and for with a MAPE value of . Successive forecasting results in January 2023 are January 2023 is 3.503,201 million USD, February 2023 is 3.577,731 million USD, and March 2023 is 3.612,201 million USD.

Crime Dynamics in Russia - Model Analysis

Introduction. The article proposes a mathematical model of the dynamics of one of the indicators of crime ― the number of persons who have committed crimes. The indicator is aggregated, so it is considered as a generalized characteristic of the behavior of crime in general. The analysis of the dynamics of the indicator in the context of the political, economic and legal situation in the country and the world is carried out.Materials and methods. As a statistical base for the crime indicator, data from the statistical yearbook "Russia in figures" has been used. For global oil demand, data from the monthly review of the International Energy Agency (Monthly Oil Market Report // International Energy Agency) has been used. To build mathematical models, time series analysis and modeling methods were used, and correlation analysis methods were used when comparing models. General scientific research methods were also used - systematic and historical approaches.Results of the study. A mathematical model is proposed, which is the sum of periodic functions and a trend. The functions have several settings and only one argument – time. The model adequately describes the behavior of the indicator therefore it can be used to obtain short-term forecasts. The historical period is highlighted, in which the correlation between the dynamics of crime and the dynamics of the global economic situation is traced. The stabilization of crime dynamics in recent years has been noted, when the processes of restoring Russia's economic sovereignty accelerated.Discussion and conclusion. The proposed mathematical model gives good results of fitting the crime indicator to a time series on the entire observation period. And this period covers very different historical situations: from unlimited entry into the global economic system to strengthening economic sovereignty. The reliability of the model over the entire interval indicates the validity of its use even with new economic benchmarks. The result of demonstrating a rapid decrease in the amplitude of fluctuations in the crime rate while strengthening the sovereignty of the Russian economy in recent years is indicative. The processes that were analyzed in the work have many differences, but they are well described by models of the same type. A possible explanation for this invariance is the hypothesis that some complex socio-economic processes not only affect each other's condition, but synchronize their dynamics.

Nuclear quantum effects on the vibrational dynamics of the water-air interface.

We have applied path-integral molecular dynamics simulations to investigate the impact of nuclear quantum effects on the vibrational dynamics of water molecules at the water-air interface. The instantaneous fluctuations in the frequencies of the O-H stretch modes are calculated using the wavelet method of time series analysis, while the time scales of vibrational spectral diffusion are determined from frequency-time correlation functions and joint probability distributions. We find that the inclusion of nuclear quantum effects leads not only to a redshift in the vibrational frequency distribution by about 120 cm-1 for both the bulk and interfacial water molecules but also to an acceleration of the vibrational dynamics at the water-air interface by as much as 35%. In addition, a blueshift of about 45 cm-1 is seen in the vibrational frequency distribution of interfacial water molecules compared to that of the bulk. Furthermore, the dynamics of water molecules beyond the topmost molecular layer was found to be rather similar to that of bulk water.

Real-time collision detection based on external torque mutation suppression and time series analysis

PurposeAs the demand for human–robot collaboration in manufacturing applications grows, the necessity for collision detection functions in robots becomes increasingly paramount for safety. Hence, this paper aims to improve the existing method to achieve efficient, accurate and sensitive robot collision detection.Design/methodology/approachThe external torque is estimated by momentum observers based on the robot dynamics model. Because the state of the joints is more accessible to distinguish under the action of the suppression operator proposed in this paper, the mutated external torque caused by joint reversal can be accurately attenuated. Finally, time series analysis (TSA) methods can continuously generate dynamic thresholds based on external torques.FindingsCompared with the collision detection method based only on TSA, the invalid time of the proposed method is less during joint reversal. Although the soft-collision detection accuracy of this method is lower than that of the symmetric threshold method, it is superior in terms of detection delay and has a higher hard-collision detection accuracy.Originality/valueOwing to the mutated external torque caused by joint reversal, which seriously affects the stability of time series models, the collision detection method based only on TSA cannot detect continuously. The consequences are disastrous if the robot collides with people or the environment during joint reversal. After multiple experimental verifications, the proposed method still exhibits detection capabilities during joint reversal and can implement real-time collision detection. Therefore, it is suitable for various engineering applications.