Statistical Analysis Of Time Series Research Articles

Online graph topology learning from matrix-valued time series

The focus is on the statistical analysis of matrix-valued time series, where data is collected over a network of sensors, typically at spatial locations, over time. Each sensor records a vector of features at each time point, creating a vectorial time series for each sensor. The goal is to identify the dependency structure among these sensors and represent it with a graph. When only one feature per sensor is observed, vector auto-regressive (VAR) models are commonly used to infer Granger causality, resulting in a causal graph. The first contribution extends VAR models to matrix-variate models for the purpose of graph learning. Additionally, two online procedures are proposed for both low and high dimensions, enabling rapid updates of coefficient estimates as new samples arrive. In the high-dimensional setting, a novel Lasso-type approach is introduced, and homotopy algorithms are developed for online learning. An adaptive tuning procedure for the regularization parameter is also provided. Given that the application of auto-regressive models to data typically requires detrending, which is not feasible in an online context, the proposed AR models are augmented by incorporating trend as an additional parameter, with a particular focus on periodic trends. The online algorithms are adapted to these augmented data models, allowing for simultaneous learning of the graph and trend from streaming samples. Numerical experiments using both synthetic and real data demonstrate the effectiveness of the proposed methods.

Statistical analysis of multivariate binary time series based on a neural network model

This article is devoted to the statistical analysis of multivariate binary time series. For solving this problem a parsimonious neural network model of Markov’s ergodic chain of order s was determined. Consistent statistical estimators for model parameters and estimation algorithms of parameters and forecasting algorithms of future states of time series were developed. The results of computer experiments on simulated and real data are presented.

Accurate Price prediction by Double Deep Q-Network

For more than several decades, time series data have been in the center of attention for scholars to predict the future prices of the markets, the most fundamental and challenging of which has been the prediction of the price of the stock market. It is of great importance to note that the algorithms with the fewest errors in price predictions are more applicable. There have been more methods suggested for price prediction in the stock markets: time series data analysis, mathematical and statistical analysis, signal processing, pattern recognition and machine learning. One of the demerits of the aforementioned methods is failing to recognize sudden change of prices, in this regard, experiencing more errors is the consequence of such demerit. In this regard, to have the error solved, the DDQN algorithm, consisting of deep neural networks which includes LSTM-CNN layers, has been employed. Confronting price fluctuations, the agent has the privilege of having better performance by employing the advantages of LSTM-CNN layers. In this research, the algorithm has been carried out over Iranian Gold Market, including six various types of Gold, from 2009 to 2020. The results reveal the point that the given method is more precise in comparison with other suggested methods confronting sudden changes in prices.

Rapidly Developing a Community- and Evidence-Based Heat Action Plan

Abstract Extreme heat contributes to 8000–12 000 excess U.S. deaths per year. Partly due to increasing summer temperatures and a renewed focus on environmental justice, local governments started new initiatives to manage and adapt to extreme heat. For example, Miami-Dade County, Florida, United States, appointed Jane Gilbert as the world’s first Chief Heat Officer. This manuscript summarizes Miami-Dade County’s preliminary efforts to build local evidence, engage the community, and rapidly respond to extreme heat. The manuscript’s goal is to expedite the translation of existing tools into mainstream extreme heat, health, and equity planning. The study generated local evidence to identify the places and periods of time with elevated heat-related illness using a statistical vulnerability and time series analysis, respectively. The places with the highest severe heat-related illness rates had hotter land surface temperatures and/or higher proportions of people who were outdoor workers, Indigenous, living in poverty or mobile homes, and households with children. “Everyday” summer conditions instead of rare heatwaves increase the risk of a heat-related death. The Chief Heat Officer convened workshops that engaged 298 unique community members on six cross-sectoral heat topics. Key recommendations included increasing multisectoral heat monitoring and risk communication, building more affordable housing, preserving and expanding greenspace, and creating heat resilience hubs. The activities culminated in a Heat Action Plan, which was completed in less than two years from the receipt of project funding.

Time Series Analysis and Algorithm for Fluctuation Pattern Recognition and Forecasting of Economic Indicators

This study examines the use of artificial neural network (ANN) algorithms in machine learning for recognizing fluctuation patterns and forecasting economic indicators in the field of political economy. In today's changing global economic scene, economic forecasting is critical for informing policy decisions, directing strategic planning, and managing risks. Traditional statistical approaches and time series analysis have long been used for this purpose, but the advent of ANN algorithms provides a new way to capture the intricate dynamics of economic systems. Using ANNs' intrinsic flexibility and nonlinear modelling capabilities, this study analyzes their ability to effectively forecast future swings in economic indices. This work intends to understand the strengths, limits, and practical consequences of ANN-based forecasting models by conducting a complete assessment of literature, methodological methods, and empirical studies. This study adds to the continuing discussion of economic forecasting approaches by diving into theoretical frameworks, methodological concerns, and empirical data. Finally, incorporating ANN algorithms into machine learning offers promising opportunities for improving the understanding of economic processes and supporting informed decision-making in the political economy.

New graphical models for sequential data and the improved state estimations by data-conditioned driving noises

A prevalent problem in statistical signal processing, applied statistics, and time series analysis arises from the attempt to identify the hidden state of Markov process based on a set of available noisy observations. In the context of sequential data, filtering refers to the probability distribution of the underlying Markovian system given the measurements made at or before the time of the estimated state. In addition to the filtering, the smoothing distribution is obtained from incorporating measurements made after the time of the estimated state into the filtered solution. This work proposes a number of new filters and smoothers that, in contrast to the traditional schemes, systematically make use of the process noises to give rise to enhanced performances in addressing the state estimation problem. In doing so, our approaches for the resolution are characterized by the application of the graphical models; the graph-based framework not only provides a unified perspective on the existing filters and smoothers but leads us to design new algorithms in a consistent and comprehensible manner. Moreover, the graph models facilitate the implementation of the suggested algorithms through message passing on the graph.

Reducing intravenous antibiotics in neonates born ≥35 weeks' gestation: A quality improvement study.

To assess the impact of the Early Onset Sepsis (EOS) calculator, implemented as a quality improvement study, to reduce the rate of unnecessary antibiotics in neonates born ≥35 weeks' gestation. An audit of routinely collected hospital data from January 2008 to March 2014 (retrospective) and from January 2018 to September 2019 (prospective) determined baseline incidence of EOS intravenous antibiotic use in neonates born ≥35 weeks' gestation in a tertiary level perinatal centre. Plan-do-study-act (PDSA) cycles were applied to implement the EOS calculator. Statistical process control methodology and time series analysis assessments were used to assess the potential impact of the PDSA cycles on the rate of intravenous antibiotics, blood culture collection, EOS, length of stay and health care costs (not adjusted for potential confounders). In the study population, from January 2008 to March 2014, the baseline incidence of intravenous antibiotic use was 10.49% (2970/28290), whilst only 0.067% (19/28290) neonates had culture proven EOS. From January 2018 to October 2019, prior to implementation of the EOS calculator, 13.3% (1119/8411) neonates were treated with intravenous antibiotic and the use decreased to 8.3% (61/734) post-implementation. The rate of blood culture collection decreased from 14.4% (1211/8411) to 11.9% (87/734). There were no cases of missed EOS. Length of stay decreased from 2.68 to 2.39 days, with an estimated cost saving of $366 per patient per admission. Implementing the EOS calculator in a tertiary hospital setting reduced invasive investigations for EOS and intravenous antibiotic use among neonates ≥35 weeks' gestation. This can result in reduced length of neonatal hospital stays, and associated health care cost savings and may reduce separation of mother and baby.

Analysis of Pros and Cons in Using the Water–Energy–Food Nexus Approach to Assess Resource Security: A Review

The water–energy–food (WEF) nexus is drawing much attention in scholarly literature as a novel alternative to address complex resources and achieve resource security. The aim of this study is to analyze and review existing nexus studies to investigate the current status of nexus research worldwide. This study used a narrative review approach to provide a comprehensive overview on the WEF nexus using a variety of databases. It is indicated that the majority of studies in Asia and Africa focused on the water–energy–food (WEF) nexus. China and Brazil had the largest nexus research. Based on the existing literature, most of attention has been paid to food production. However, food consumption patterns and dietary change are rarely evaluated, and there is a lack of study on impacts of dietary change on the WEF nexus. Moreover, there is a lack of frameworks for the evaluation of the WEF nexus under dietary change scenarios. The major challenge of the nexus approach is data availability in crop production, which can be solved by using remote sensing data. There is a lack of standard and conceptual frameworks for nexus assessment and, then, an essential need to provide a new holistic and standard approach that be applicable worldwide to increase connections between researchers and decision makers, as well as the applicability of nexus approaches. Future research must couple the development of a holistic standard approach with experimental tests in different areas, involving interdisciplinary research groups able to carry out all the experimental activities, the numerical simulations, and the statistical analyses of climatic time series (in a climate change perspective) indispensable to demonstrate the real benefits of using a WEF-derived nexus approach.

War in Ukraine: Impact on global agri-food trade

The conditions and trends of the global agri-food market are elastic to crises and conflicts involving major market players, which threatens food security by blocking sustainable international trade opportunities due to the destruction of production capacity and value chains, as well as supply channels to consumer countries. The purpose of this study was to investigate the specific features and consequences of the impact of the Russian-Ukrainian armed conflict on world agricultural trade and food security at the national and global levels. The scientific conclusions are the result of the application of economic and statistical (time series, index, and factor analysis) and dialectical (induction and deduction, analysis, and synthesis) methods of scientific cognition. It was found that the universal features of wars include the ability to influence the global economy, create new geopolitical and economic uncertainty, rapidly increase prices for critical resources (energy), and ensure destabilisation of global value chains. As a result, the disruption of sustainable trade flows caused by military conflicts is an important source of changes in the global economy and the situation of individual countries and regions. It was proved that military operations on the territory of Ukraine led to restrictions in agriculture and forced adaptation of agribusiness to the factors of critical vulnerability. There are trends towards a reduction in production and the breakdown of conventional supply chains for agri-food products due to the destruction of the logistics infrastructure, which, together with a reduction in exports of agricultural products and fertilisers from Russia, has led to a rise in world food prices. As a result, the “price” of the Russian-Ukrainian war for the global economy is being formed, with growing hunger and poverty, as well as the environmental footprint of military operations. The practical significance of the findings of this study lies in the possibility of their application in strategic planning and substantiation of the areas of post-war reconstruction of the national economy in general and its agricultural sector specifically, as well as the restoration and strengthening of Ukraine’s role in global food chains and combating hunger

Анализ динамики селей на фоне климатических изменений в высокогорной зоне Кабардино-Балкарии

The object of the study is a dangerous slope phenomenon (mudflow) in the mountains of the Kabardino-Balkarian Republic (KBR) against the background of changing climatic conditions of the warm season (May-September). The paper presents the results of statistical, frequency and regression analysis of the dynamics of the number of mudflows, precipitation amounts, and average temperatures in the high-mountain zone of the KBR in the period 1953–2015. Statistical analysis of time series was carried out using the SPSS 20.0 software package. Linear trends were generated using Excel built-in LINEST worksheet function. The efficiency (coefficient of determination R2 ) and quality of regression models in general and by parameters were assessed using the Fisher test (F) and their significance (Sig.). The t-test was used to identify statistical equality or inequality of means between two periods. It was found that in the period 1953–2015 there was a slight increase in precipitation and average temperature. When analyzing the statistics of series in two periods 1953–1984 and 1984–2015, it was found that the average and total characteristics of the number of mudflows, precipitation and temperatures increase in the second period. Frequency analysis showed that out of 499 cases of mudflows over 63 years, 32% occurred in 9 cases per season (May-September). Frequency analysis confirmed the homogeneity of the distribution of precipitation amounts: in almost 60% of cases, the precipitation amount varies in a narrow range from 92.7 mm to 104.1 mm. For a number of temperatures, the most common (37%) was the average temperature of the warm season t = 10.2 °C; 31% corresponded to the temperature range from 10.7 °C to 11.8 °C. The analysis allows us to talk about an increase in the frequency of dangerous slope phenomena against the background of modern climate change, including in the high mountains.

Estimating the opening stock price index for Indian market under the influence of varying crude oil and Dollar values through statistical time series analysis

Estimating the opening stock price index for Indian market under the influence of varying crude oil and Dollar values through statistical time series analysis

Prophet와 GRU을 이용하여 단중기 전력소비량 예측

The building energy management system (BEMS), a system designed to efficiently manage energy production and consumption, aims to address the variable nature of power consumption within buildings due to their physical characteristics, necessitating stable power supply. In this context, accurate prediction of building energy consumption becomes crucial for ensuring reliable power delivery. Recent research has explored various approaches, including time series analysis, statistical analysis, and artificial intelligence, to predict power consumption. This paper analyzes the strengths and weaknesses of the Prophet model, choosing to utilize its advantages such as growth, seasonality, and holiday patterns, while also addressing its limitations related to data complexity and external variables like climatic data. To overcome these challenges, the paper proposes an algorithm that combines the Prophet model's strengths with the gated recurrent unit (GRU) to forecast short-term (2 days) and medium-term (7 days, 15 days, 30 days) building energy consumption. Experimental results demonstrate the superior performance of the proposed approach compared to conventional GRU and Prophet models.

Space-temporal structure of the thunderstorm ground enhancements (TGEs)

We analyzed the structure of the Thunderstorm Ground Enhancement (TGE) using a particle detector network on Aragats. We performed a statistical analysis of the particle arrival time series on a nanosecond time scale using the largest TGE event on record, which occurred on May 23, 2023. Our findings confirm that the TGE is a mixture of multiple runaway electron avalanches that arrive independently and provide stable particle flux. The electron accelerator, operated by the dipole that emerges in the lower part of the thundercloud, sends copious electrons and gamma rays toward the Earth's surface that sustains for minutes. The experimental results are supported by simulations of electron multiplication and acceleration in strong atmospheric electric fields. We compare TGEs and Terrestrial Gamma Flashes (TGFs), which are brief bursts observed by gamma-ray detectors in orbit and are thought to be associated with atmospheric discharges.

Identification and quantification of nutrients sources in the Aspio watershed (Italy). Insight from geogenic mineralization and anthropogenic pressure

An accurate evaluation of river water quality could be challenging due to the complex hydrological and anthropogenic processes which affect its nature. Reliable water quality data are mandatory to identify long-term trends and regional variability at the watershed scale. In this study, a combined approach using time series, regression, and multivariate statistical analysis along with SWAT modelling was applied to identify the relevant hydrogeochemical processes and the nutrients sources within the Aspio watershed (Ancona, Italy). The analysis detected different processes: i) the geogenic origin of Cl- and SO42-, ii) the heavy metals (Cu and Ni) and hydrocarbons pollution due to runoff from urban and industrial areas, and iii) the agricultural contribution of pesticides, nitrogen, and phosphorous. A SWAT model was implemented to quantify the nutrients load in the Aspio river. A calibration for streamflow, river sediment yield, and for nutrients load was obtained considering agricultural, urban, and wastewater treatment plant contributions. Agriculture and treated wastewater contributed to the overall nitrogen load only for 4% and 12% respectively, while the majority was due to leakage from urban sewage (84%). A scenario with only fertilizers’ load (excluding other sources) highlighted that nitrogen and phosphorous export from agricultural lands did not significatively impact the Aspio river. The spatial representation of runoff susceptibility also showed how the highest susceptibility for nitrogen and phosphorous loads is due to areas located close to urban settlements.

A Statistical Analysis of Forensic Autopsies Performed in the Jungbu Province of Korea in 2021

We analyzed 1,079 forensic autopsies conducted in 2021 by the National Forensic Service Daejeon Institute from the Jungbu province in the central part of South Korea, for their manner and cause of death. Among the manner of death (n=1,079), 45.3% (n=489) were categorised as unnatural, 39.5% (n=426) as natural, and 15.2% (n=164) were unknown. Among the unnatural deaths (n=489), 40.7% (n=199) were accidents, 36.8% (n=180) were suicides, 14.1% (n=69) were undetermined, and 8.4% (n=41) were homicides. The major causes of unnatural deaths (n=489) were by trauma, causing 34.4% (n=168), poisoning causing 26.8% (n=131), and asphyxia causing 17.2% (n=84). The major cause of death by trauma (n=168) was falling at 46.4% (n=78), and by asphyxia (n=84) was strangulation at 76.2% (n=64). Among natural deaths (n=426), heart disease was the major cause at 49.8% (n=212) followed by vascular disease at 16.7% (n=71). In future, a time-series statistical analysis on the manner and causes of death in Jungbu province may provide insight and allow for more advanced interpretations about both healthcare and public safety.

Self‐assembled vapor-transport-deposited SnS nanoflake-based memory devices with synaptic learning properties

The most salient features of resistive switching (RS) devices are low energy consumption, fast switching speed, and high-density integration, which render them promising candidates for realizing non-volatile memory and artificial synaptic devices. However, the growth of functional switching layers for RS devices needs innovative deposition techniques. Herein, we utilize a high-throughput vapor-transport-deposition (VTD) technique for synthesizing self-assembled tin-sulfide (SnS) nanoflakes, which are then used as a switching layer to fabricate an RS device. First principle calculations are conducted to understand the optoelectronic properties of SnS by employing density functional theory. The proposed Ag/SnS/Pt memory device exhibits substantial merits, including low-switching voltages (VSET: 0.22 V and VRESET: −0.20 V), suitable ON/OFF ratio (∼259), excellent endurance (106), and extended memory retention (106 s) characteristics. In addition, RS stochasticity is modeled using statistical time-series analysis via Holt’s exponential smoothing. Interestingly, the device can emulate multiple synaptic functionalities, including potentiation, depression, paired-pulse facilitation, paired-pulse depression, excitatory postsynaptic current, inhibitory postsynaptic current, and advanced spike-timing dependent plasticity rules. Moreover, the proposed synaptic device can detect the edge of images by utilizing a convolutional neural network. The unique and efficient VTD-SnS-based device will be a potential candidate for high-density non-volatile memory and neuromorphic computing applications.

Long-term variability of the thermal state in the upper horizons of the cryolithozone in Central Yakutia

The results of the 42-year monitoring of the thermal state of permafrost landscapes in modern climate warming conditions at the “Chabyda” observation station (Central Yakutia) are presented. We studied the evolution of the variability of soil thermal state parameters: the temperature of soils on the sole of the seasonally shallow layer and in the layer of annual heat rotations, and the depth of seasonal thawing. The long-term variability of the average annual soil temperature, experiencing significant interannual and short-term fluctuations, has a multidirectional trend, with warming dominating. The main factors regulating the thermal state of permafrost landscapes are the short-period fluctuations in the snow accumulation regime. Despite significant interannual fluctuations, the depth of seasonal thawing responded to climate warming with multidirectional trends, and its increase or decrease was statistically insignificant. We performed a statistical analysis of the observation time series revealing correlations between climate elements (air temperature, total precipitation, average winter snow depth, duration of winter and summer seasons) and relationships between soil temperature regime parameters (Тξ, Т0, ξ) with climate elements. The results of these studies on the thermal state of soils can be applied to homogeneous landscapes in this region. They can also be used to model heat transfer processes in natural landscapes and develop hybrid intelligence methods for diagnosing the state of infrastructure facilities in the Far North based on high-performance computing systems.

Storage requirements to mitigate intermittent renewable energy sources: analysis for the US Northeast

Moving away from fossil fuels is essential for a sustainable future. Carrying out this transition without reversing the improvements in the quality of life is the ultimate challenge. While minimizing the anticipated impacts of climate change is the primary driver of decarbonization, the inevitable exhaustion of fossil energy sources should provide just as strong or perhaps even stronger incentives. The vast majority of publications outlining the pathways to “net-zero carbon emission” fall short from leading to a truly “fossil fuel-free” future without falling back to some level of dependence on fossil fuels with carbon capture and sequestration. While carbon capture and sequestration might be a necessary step toward decarbonization, such intermediate goals might turn into a dead end without defining the end point. The main obstacle to wider adoption of renewable energy resources is their inherent intermittency. Solar and wind are, by far, the most abundant renewable energy sources that are expected to take the lion share in transitioning to a sustainable future. Intermittency arises at multiple levels. The most recognized are the short-term (minute-by-minute, hourly, or diurnal) variations that should be the easiest to address. Less frequently realized are the seasonal and inter-annual variabilities. Seasonality poses far greater challenges than minute-by-minute or hourly variations because they lead to the absence of energy resources for prolonged periods of time. Our interest is the feasibility of a future where all energy (100%) comes from renewable sources leaving no room for fossil fuels. We carry out rudimentary statistical analyses of solar radiation and wind speed time series records to quantify the degree of their intermittencies seasonally and inter-annually. We employ a simple but robust accounting of the shortfalls when the supplies do not meet demand via a modified cumulative supply/deficit analysis that incorporates energy losses arising from transporting excess energy to storage and retrieving it as needed. The presented analysis provides guidance for choosing between the installation of excess capacity or the deployment of energy storage to guarantee reliable energy services under the assumption that the energy system is powered exclusively by renewable energy sources. This paper examines the seasonal and inter-annual variability of hydropower and biofuel resources to estimate their potential to mitigate the intermittencies of solar and wind resources. The presented analyses are meant to provide crude, bulk part estimates and are not intended for planning or operational purposes of the actual energy infrastructures. The primary focus of this paper is the Northeast region of the United States using the conterminous United States as a reference to assess the viability of reducing the energy storage need in the study region via improved connectivity to the national grid. This paper builds on the modeling exercises carried out as part of the climate-induced extremes on food, energy, water systems studies.

Winter Climate of Northeastern Dominican Republic and Cash Crop Production

The winter climate of the northeastern Dominican Republic features steady rainfall, which sustains cash crop production. Using a representative season, December 2016–February 2017, the mesoscale climate is characterized by high-resolution reanalysis, satellite measurements and local observations, and statistical analyses of time series from an index area of 18.8–19.6° N, 70.4–69.6° W in the Cibao Valley, where cacao and coffee are grown. Winter rainfall depends on strong trade winds that push shallow stratiform convections over 100 km inland, where nocturnal drainage flows induce orographic uplift. Interannual climate variability is studied in the context of cacao and coffee production in the years 1976–2019. Lag correlations demonstrate that higher yields follow a wet autumn, a windy winter with cool sea temperatures, and a dry spring. Changes in high-value agricultural production in the northeastern Dominican Republic may be anticipated by the climatic determinants uncovered here.

ANALYSIS OF STOCK MARKET DATA FOR GUIDANCE OF STOCK INVESTORS

Analysis of stock market data is essential for enabling stock investors to make wise choices and create winning investment plans. The significance and advantages of applying data analysis techniques for stock market investors are discussed in this abstract.The abstract opens by emphasizing the stock market's rising complexity and volatility, which calls for the use of sophisticated analytical techniques. In order for investors to recognize trends, evaluate risk, and take advantage of opportunities, it highlights the significance of accurate and timely information.The abstract then goes on to describe different data analysis techniques used in stock market analysis. These techniques include sentiment analysis, machine learning, time series forecasting, statistical analysis, and data visualization. It highlights their importance in discovering correlations, deriving meaningful patterns, and forecasting future market moves.The abstract also highlights the importance of historical data research for investors. Data from the historical stock market can be used to gain understanding of current trends, market behavior, and the performance of certain stocks or industries. Investors may make wise judgments, create investing strategies, and efficiently manage risk with the help of these insights.The abstract also emphasizes how crucial alternate data sources are becoming to stock market analysis. Technology has given investors access to a wide range of non-traditional data, including sentiment on social media, news stories, and satellite imagery. Integrating these non-traditional data sources with conventional financial data might give investors new perspectives and a competitive edge.The abstract also recognizes how technology helps stock investors analyze data more easily. Big data processing, artificial intelligence algorithms, and advanced computational approaches are transforming how investors read and analyze enormous volumes of data. Investors can rapidly and effectively get useful insights thanks to these technologies.Finally, the abstract stresses the revolutionary potential of stock market data analysis in its conclusion. Investors can reduce risks, find untapped possibilities, and improve their investing strategies by utilizing data-driven methodologies. Utilizing the power of data analysis is essential for stock investors to manage the complexity and meet their investing goals in a constantly changing and dynamic market. Key words: stock market, data analysis, traders, traders' strategies, alternative data sources, computational methods, artificial intelligence, risk management.