Time Series Approach Research Articles

Multitimescale Template Matching: Discovering Eruption Precursors across Diverse Volcanic Settings

Abstract Volcanic eruptions pose significant risks, demanding precise monitoring for timely hazard mitigation. However, interpreting noisy seismic data for eruptive precursors remains challenging. This study introduces a novel methodology that extends an earlier time-series feature engineering approach to include template matching against prior eruptions. We aim to identify subtle signals within seismic data to enhance our understanding of volcanic activity and future hazards. To do this, we analyze the continuous seismic record at a volcano and identify the time-series elements that regularly precede eruptions and the timescales over which these are observable. We conduct tests across various time lengths, ranging from 1 to 60 days. For Copahue (Chile/Argentina), Pavlof (Alaska), Bezymianny (Russia), and Whakaari (New Zealand) volcanoes, we confirm statistically significant eruption precursors. In particular, a feature named change quantiles (0.2–0.8), which is related to the conditional dynamics of surface acceleration at the volcano, emerges as a key indicator of future eruptions over 14-day timescales. This research offers new methods for real-time seismovolcanic monitoring, minimizing the effects of unknown, spurious noise, and discerning recurrent patterns through template matching. By providing deeper insights into pre-eruptive behavior, it may lead to more effective hazard reduction strategies, enhancing public safety around active volcanoes.

Association of sudden sensorineural hearing loss with meteorological factors: a time series study in Hefei, China, and a literature review

Air pollution can cause disease and has become a major global environmental problem. It is currently believed that air pollution may be related to the progression of SSNHL. As a rapidly developing city in recent years, Hefei has serious air pollution. In order to explore the correlation between meteorological variables and SSNHL admissions, we conducted this study. This study investigated the short-term associations between SSNHL patients admitted to the hospital and Hefei climatic variables. The daily data on SSNHL-related hospital admissions and meteorological variables containing mean temperature (T-mean; °C), diurnal temperature range (DTR; °C), atmospheric pressure (AP; Hp), and relative humidity (RH; %), from 2014 to 2021 (2558 days), were collected. A time-series analysis integrating distributed lag non-linear models and generalized linear models was used. PubMed, Embase, Cochrane Library, and Web of Science databases were searched. Literature published up to August 2023 was reviewed to explore the potential impact mechanisms of meteorological factors on SSNHL. The mechanisms were determined in detail, focusing on wind speed, air pressure, temperature, humidity, and air pollutants. Using a median of 50.00% as a baseline, the effect of exceedingly low T-mean in the single-day hysteresis effect model began at a lag of 8 days (RR = 1.032, 95% CI: 1.001 ~ 1.064). High DTR affected the admission rate for SSNHL on lag 0 day. The significance of the effect was the greatest on that day (RR = 1.054, 95% CI: 1.007 ~ 1.104) and then gradually decreased. High and exceedingly high RH affected the admission rate SSNHL on lag 0 day, and these effects lasted for 8 and 7 days, respectively. There were significant associations between all grades of AP and SSNHL. This is the first study to assess the effect of meteorological variables on SSNHL-related admissions in China using a time-series approach. Long-term exposures to high DTR, RH values, low T-mean values, and all AP grades enhance the incidence of SSNHL in residents. Limiting exposure to extremes of ambient temperature and humidity may reduce the number of SSNHL-related hospital visits in the region. It is advisable to maintain a suitable living environment temperature and avoid extreme temperature fluctuations and high humidity. During periods of high air pollution, it is recommended to stay indoors and refrain from outdoor exercise.

Collaborative Counseling to Build a Social Construct to Overcome Alcoholic Addiction

Alcohol consumption by teenagers is influenced by environmental factors. To overcome alcohol addiction among teenagers, a social construction needs to be built. So efforts are needed to overcome alcohol addiction, namely through collaborative counseling. In Collaborative Counseling the relationships and conversations create something different bringing together the collection, application, innovation and expansion of ideas involving working professionals from various disciplines, contexts and cultures, by detailing what they actually do every day for individuals, couples, families and groups . This research uses a Mixed Method with the Triangular Design type, namely collecting quantitative and qualitative data simultaneously. Quantitative research was conducted to answer the first research question, how addicted the sample was to alcohol during the research. By using Time-series Design with a simple interrupted Time Series approach with a sample size of 8 people. The qualitative research used in this research is a phenomenological approach, namely research that answers what certain phenomena are about. This research will also be used to interpret meaning in context. From the results obtained in this research, it was found that Collaborative Counseling was successful in eliminating addiction in the research sample.

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.

Potential yields of two staple cereal crops worldwide under global warming

Most studies on model-estimated yield potential of crop under climate change are based on the principle of production function, for specific variety, from static biological dimension and at local or regional level, while few theoretically are based on time-series approach for world crop from dynamic evolutionary angle and on global scale. Therefore we conducted following research in 2019 to 2023 at Guiyang City of China: potential yields of rice and wheat worldwide by 2030 are projected creatively using Auto-regressive Integrated Moving Average and Trend-regressed model based on their history since 1961, in which the projections are tested by actual yields in recent two years and further validated by Gray System model; the impacts of global warming on past production of rice and wheat worldwide since 1961 are analyzed using binary regression model in which global mean temperature is treated as the independent variable while the crop yield as the dependent variable. Our results show that: in the future between 2022 and 2030, average yield of world rice is projected to be from 4876 kg/ha to 5195 kg/ha whereas that of world wheat from 3548 kg/ha to 3817 kg/ha; top (national) yield of world rice is projected to be from 10148 kg/ha to 10269 kg/ha whereas that of world wheat from 9914 kg/ha to 10042 kg/ha; or the ratio between average and top yields of world rice will be from 48.05% to 50.59% whereas that of world wheat from 35.79% to 38.01%; since 1961, global warming exerts a negative impact on average yield of world rice less than on its top, and a positive effect on average yield of world wheat while a negative one on its top, which partially drives the gap between average and top yields of world rice slightly narrowed and that of world wheat gradually closed. These findings indicate that: given that top yield is considered potential limit of average yield, to improve global production of these two staple cereal crops by 2030, the priorities should be given to rice as well as wheat worldwide in their both high and low yield countries.

Influence of Capital Structure and Profitability on The Company Value of PT. Waskita Karya (Persero), TBK (Period 2016 To 2020)

This study focuses on the specific time period of 2016-2020 and aims to ascertain and examine the impact of capital structure and profitability on the valuation of manufacturing companies listed on the Indonesia Stock Exchange. The independent variable X1 represents the capital structure, precisely measured by the debt-equity ratio (DER). The independent variable is the company PT. Waskita Karya (Persero), Tbk, a prominent manufacturing company, throughout the period of 2016-2020. The study population consisted of 12 years' worth of financial reports from PT Waskita Karya (Persero), Tbk, encompassing quarters 1-4. This totaled 48 financial reports. The sample for analysis was selected using the time series approach, resulting in 20 financial reports from 2016 to 2020. The data testing strategy employs several linear regression analysis methods, utilizing an alpha significance level of 5%. The findings of this study demonstrate that the capital structure has a notable adverse impact on the firm's value, reducing it by 22.4%. Conversely, profitability substantially positively influences the company's value, increasing it by 30.8%. Both capital structure and profitability have a 53.2% impact on firm value at the same time.

Prioritizing cervical cancer candidate genes using chaos game and fractal-based time series approach.

Cervical cancer is one of the most severe threats to women worldwide and holds fourth rank in lethality. It is estimated that 604, 127 cervical cancer cases have been reported in 2020 globally. With advancements in high throughput technologies and bioinformatics, several cervical candidate genes have been proposed for better therapeutic strategies. In this paper, we intend to prioritize the candidate genes that are involved in cervical cancer progression through a fractal time series-based cross-correlations approach. we apply the chaos game representation theory combining a two-dimensional multifractal detrended cross-correlations approach among the known and candidate genes involved in cervical cancer progression to prioritize the candidate genes. We obtained 16 candidate genes that showed cross-correlation with known cancer genes. Functional enrichment analysis of the candidate genes shows that they involve GO terms: biological processes, cell-cell junction assembly, cell-cell junction organization, regulation of cell shape, cortical actin cytoskeleton organization, and actomyosin structure organization. KEGG pathway analysis revealed genes' role in Rap1 signaling pathway, ErbB signaling pathway, MAPK signaling pathway, PI3K-Akt signaling pathway, mTOR signaling pathway, Acute myeloid leukemia, chronic myeloid leukemia, Breast cancer, Thyroid cancer, Bladder cancer, and Gastric cancer. Further, we performed survival analysis and prioritized six genes CDH2, PAIP1, BRAF, EPB41L3, OSMR, and RUNX1 as potential candidate genes for cervical cancer that has a crucial role in tumor progression. We found that our study through this integrative approach an efficient tool and paved a new way to prioritize the candidate genes and these genes could be evaluated experimentally for potential validation. We suggest this may be useful in analyzing the nucleotide sequences and protein sequences for clustering, classification, class affiliation, etc.

Forecasting Electricity Demand in Greece: A Functional Data Approach in High Dimensional Hourly Time Series

Forecasting Electricity Demand in Greece: A Functional Data Approach in High Dimensional Hourly Time Series

Closing the Efficiency Gap: Insights into curbing the direct rebound effect of residential electricity consumption in Saudi Arabia

The rebound effect refers to the phenomenon where energy efficiency improvements lead to increased energy consumption, offsetting some of the expected energy savings. This article aims to estimate the direct rebound effect of residential electricity consumption in Saudi Arabia and explores potential solutions to mitigate its impact. Understanding the magnitude of the rebound effect is crucial for policymakers to develop effective energy conservation strategies. This study employs a time-series modeling approach, using a dataset covering 31 years, 1990–2021, and four regions within Saudi Arabia, incorporating various socioeconomic and energy-related variables. The magnitude of the rebound effect in residential electricity consumption in the Kingdom, which varies across regions, ranges from 41% to 71%. Since the inception of the first wave of the energy price reform in 2016, the Kingdom has witnessed a power consumption rebound in the residential sector totaling 39.02 TWh (6.5 Twh per annum), and a total of 28.13 Mt. in CO2 equivalent emissions (4.69 Mt. in CO2 equivalent per annum) during the period of 2016–2021. These findings add to the existing literature on the rebound effect, further enriching our understanding of this complex phenomenon and its implications. They also provide valuable insights for policymakers and energy planners in Saudi Arabia, helping them develop effective strategies to minimize the rebound effect and promote energy conservation.

Condition Monitoring and Predictive Maintenance of Assets in Manufacturing Using LSTM-Autoencoders and Transformer Encoders.

The production of multivariate time-series data facilitates the continuous monitoring of production assets. The modelling approach of multivariate time series can reveal the ways in which parameters evolve as well as the influences amongst themselves. These data can be used in tandem with artificial intelligence methods to create insight on the condition of production equipment, hence potentially increasing the sustainability of existing manufacturing and production systems, by optimizing resource utilization, waste, and production downtime. In this context, a predictive maintenance method is proposed based on the combination of LSTM-Autoencoders and a Transformer encoder in order to enable the forecasting of asset failures through spatial and temporal time series. These neural networks are implemented into a software prototype. The dataset used for training and testing the models is derived from a metal processing industry case study. Ultimately, the goal is to train a remaining useful life (RUL) estimation model.

Income inequality analysis through complex network and nonlinear time series approaches: an Econophysics perspective

Income inequality analysis through complex network and nonlinear time series approaches: an Econophysics perspective

Impact of a nationwide school policy on body mass index in Danish school children: An interrupted time series analysis

ObjectivesA new school policy mandating 45 min physical activity daily during school was introduced in Denmark in 2014. We aimed to evaluate the effect of this policy on BMI in school-aged children. It was hypothesized that the school policy would decrease BMI, especially in the obese fraction of the population (90th percentile BMI). Study designThis register-based study was conducted as a natural experiment. MethodsAnalyses were based on data from The National Child Health Register that contains nationwide data on height and weight from mandatory preventive health examinations completed by school nurses or medical doctors during pre-preparatory classes (0th-3rd grade) and lower secondary education (7th-9th grade). A total of 401,517 children were included in the analyses with annual repeated cross-sectional data covering the period from 2012 to 2018. The effect of the school policy was evaluated using an interrupted time series approach comparing pre- and post-policy slopes in BMI, stratified by sex and age-group. ResultsIn boys, no significant differences were observed in mean BMI slopes from pre-to post-policy in either age-group. In girls, post-policy slopes were significantly higher compared to pre-policy in both age-groups (0th-3rd grade: β:0·034 kg/m2, 95%-CI: (0·024; 0·043), p-value: <0·001; 7th-9th grade: β:0·066 kg/m2, 95%-CI: (0·028; 0·103), p-value: 0·001). No significant differences in slopes were observed in BMI at the 90th percentile from pre-to post-policy for both sexes and across both age-groups. Adjustment for leisure-time physical activity as a potential time-varying confounder did not alter the findings. ConclusionsIn conclusion, we did not detect a significant decrease in BMI levels among school-aged children following the introduction of a nationwide school policy specifying daily physical activity in school. If anything, a small positive change in BMI was observed in girls. More research is needed to understand whether structural changes similar to this requirement are able to prevent overweight and obesity in children and adolescents.

The Impact of Immigration Policy Changes on Preterm Birth Rates in Texas: An Examination of Border and Nonborder Regions

BackgroundOur study examined the acute and sustained impact of immigration policy changes announced in January 2017 on preterm birth (PTB) rates among Hispanic and non-Hispanic white women in Texas's border and nonborder regions. MethodsUsing Texas birth certificate data for years 2008 through 2020, we used a multiple group interrupted time series approach to explore changes in PTB rates. ResultsIn the nonborder region, the PTB rate among Hispanic women of any race was 8.64% in 2008 and was stable each year before 2017 but increased by .29% (95% CI [.12, .46]) annually between 2017 and 2020. This effect remained statistically significant even when compared with that of non-Hispanic white women (p = .014). In the border areas, the PTB rate among Hispanic women of any race was 11.67% in 2008 and remained stable each year before and after 2017. No significant changes were observed when compared with that of non-Hispanic white women (p = .897). In Texas as a whole, the PTB rate among Hispanic women of any race was 10.16% in 2008 and declined by .07% (95% CI [−.16, −.03]) per year before 2017, but increased by .16% (95% CI [.05, .27]) annually between 2017 and 2020. The observed increase was not statistically significant when compared with that of non-Hispanic white women (p = .326). ConclusionsThe January 2017 immigration policies were associated with a sustained increase in PTB among Hispanic women in Texas's nonborder region, suggesting that geography plays an important role in perceptions of immigration enforcement. Future research should examine the impact of immigration policies on maternal and child health, considering geography and sociodemographic factors.

Liquidity Tiered Navigation Strategy: A Time Series Approach to Financial Market Stability

This article investigates the dynamic changes in liquidity stratification in financial markets, emphasizing how time series analysis can be used to forecast market liquidity. In China's financial markets, since 2014, liquidity stratification has become crucial, leading to significant differences in borrowing costs and difficulties among financial firms. Focusing on small and medium-sized banks, this study explores the origins and consequences of liquidity stratification. Moreover, it provides a comprehensive analytical approach for predicting market liquidity. This framework includes Granger causality tests, cointegration analysis, Auto regressive Integrated Moving Average (ARIMA) model, and Vector Auto regression (VAR) model. The method is rooted in a sequential, logical progression and is it eratively refined based on model outcomes. The findings of this study offer insights into the complexity of liquidity in financial markets and present a robust methodology for financial analysts and policymakers to forecast and strategize under the influence of liquidity stratification.

Enhancing spatial streamflow prediction through machine learning algorithms and advanced strategies

Forecasting and extending streamflow is a critical aspect of hydrology, especially where the time series are locally unavailable for a variety of reasons. The necessity of preprocessing, model fine-tuning, feature selection, or sampling to enhance prediction outcomes for streamflow forecasting using ML techniques is evaluated in this study. In this regard, the monthly streamflow at Pol-Chehr station is analyzed using various monthly rainfall and streamflow time series data from different stations. The results of streamflow prediction in the k-folds cross-validator approach are generally better than those of the time series approach, except when raw data with no preprocessing or feature selection is used. Applying the simple SVR model to raw data leads to the weakest result, but using the GA-SVR model on raw data significantly increases the Nash coefficient by about 215% and 72%, decreases the NRMSE by about 48% and 36% in the k-fold and time series approaches, even with no feature selection. On the other hand, standardization produces highly accurate model predictions in both the k-fold and time series approaches, with a minimum Nash coefficient of 0.83 and 0.73 during the test period in the simple SVR model, respectively. Finally, using optimization algorithms like GA to fine-tune ML models and feature selection does not always yield improved prediction accuracy, but it depends on whether raw or preprocessed data is chosen. In conclusion, combining k-fold cross-validator and preprocessing typically yields highly accurate predictive results, with an R value exceeding 93.7% (Nash = 0.83, SI = 0.55, NRMSE = 0.09), without requiring any additional fine-tuning or optimization. Using feature selection is only significant when utilizing the TS approach as well.

Does Digitalization Lead to Environmental Sustainability and Energy Efficiency in China?

In the past 20 years, there has been an increased focus on the connection between investments in digitalization, environment, and economic growth, however the effects of digitalization on energy efficiency in developing nations have received little attention in empirical researches. therefore, the goal of current research is to study the effect of digitalization on environmental sustainability and energy efficiency in China over 1995-2020. For this purpose, two separate models are formulated with energy efficiency and CO2 emissions as dependent variables and digitalization (measured by ICT technologies) as the explanatory variable. The study applies three time series approaches namely, Dynamic Ordinary Least Square (DOLS), Fully Modified Ordinary Least Square (FMOLS), and Canonical Cointegration Regression (CCR) to empirically estimate the study objectives. The study findings show that ICT plays positive role in promoting energy efficiency and environmental sustainability as coefficient of ICT is negative in both models. It was concluded from the findings that ICT sector has positive implications in terms of energy and environment in China. The study recommends the Chinese government and policy makers to pay attention and device effective policies for promotion of ICT sector in the country.

Comparing Machine Learning and Time Series Approaches in Predictive Modeling of Urban Fire Incidents: A Case Study of Austin, Texas

This study examines urban fire incidents in Austin, Texas using machine learning (Random Forest) and time series (Autoregressive integrated moving average, ARIMA) methods for predictive modeling. Based on a dataset from the City of Austin Fire Department, it addresses the effectiveness of these models in predicting fire occurrences and the influence of fire types and urban district characteristics on predictions. The findings indicate that ARIMA models generally excel in predicting most fire types, except for auto fires. Additionally, the results highlight the significant differences in model performance across urban districts, indicating an impact of local features on fire incidence prediction. The research offers insights into temporal patterns of specific fire types, which can provide useful input to urban planning and public safety strategies in rapidly developing cities. In addition, the findings also emphasize the need for tailored predictive models, based on local dynamics and the distinct nature of fire incidents.

A time series analysis of monthly issuance of plant import permits in Nigeria

An import permit is issued by the National Plant Protection Organization of each country, known in Nigeria as the Nigeria Agricultural Quarantine Service (NAQS), to allow the importation of pest-free plants and prevent the introduction of quarantine pests or check the entry of regulated non-quarantine pests of imported plant commodities. Issuance of import permits at the Post-Entry Quarantine, Surveillance and Diagnostic Station (PEQDS), Ibadan is mostly for germplasm materials mainly used for cultivation, which poses a high risk of pest introduction. Thus, this study investigated the statistical components of the issuance of import permits at the PEQDS using the Time Series approach to determine the peak period of collection essential for planning phytosanitary activities to mitigate the introduction of foreign pests. The dataset of the monthly issuance of import permits from January 2012 to July 2022 was analysed. From the preliminary study, we observed that the series exhibit seasonal and stationary components with no definite trend pattern, hence we modelled the series using a Seasonal Autoregressive Moving Average (SARIMA) model. Several models were estimated and the best model (SAR(12)) for forecasting the future import permit collectionwas selected based on the forecast accuracy measures. The forecasted values indicated the highest issuance of import permits in May and September, each year and showed a fluctuating movement over time in the future. To further prevent pest incursion and facilitate trade, NAQS needs to operate round-the-clock surveillance, keep its staff on high alert and adequate facilities operational for more effective activities. Other factors responsible for low and fluctuating issuance of import permits should also be investigated.

Demand forecasting for platelet usage: From univariate time series to multivariable models.

Platelet products are both expensive and have very short shelf lives. As usage rates for platelets are highly variable, the effective management of platelet demand and supply is very important yet challenging. The primary goal of this paper is to present an efficient forecasting model for platelet demand at Canadian Blood Services (CBS). To accomplish this goal, five different demand forecasting methods, ARIMA (Auto Regressive Integrated Moving Average), Prophet, lasso regression (least absolute shrinkage and selection operator), random forest, and LSTM (Long Short-Term Memory) networks are utilized and evaluated via a rolling window method. We use a large clinical dataset for a centralized blood distribution centre for four hospitals in Hamilton, Ontario, spanning from 2010 to 2018 and consisting of daily platelet transfusions along with information such as the product specifications, the recipients' characteristics, and the recipients' laboratory test results. This study is the first to utilize different methods from statistical time series models to data-driven regression and machine learning techniques for platelet transfusion using clinical predictors and with different amounts of data. We find that the multivariable approaches have the highest accuracy in general, however, if sufficient data are available, a simpler time series approach appears to be sufficient. We also comment on the approach to choose predictors for the multivariable models.

Different PCA approaches for vector functional time series with applications to resistive switching processes

This paper is motivated by modeling the cycle-to-cycle variability associated with the resistive switching operation behind memristors. Although the data generated by this stochastic process are by nature current–voltage curves associated with the creation (set process) and destruction (reset process) of a conductive filament, the statistical analysis is usually based on analyzing only the scalar time series related to the reset and set voltages/currents in consecutive cycles. As the data are by nature curves, functional principal component analysis is a suitable candidate to explain the main modes of variability associated with these processes. Taking into account this data-driven motivation, in this paper we propose two new forecasting approaches based on studying the sequential cross-dependence between and within a multivariate functional time series in terms of vector autoregressive modeling of the most explicative functional principal component scores. The main difference between the two methods lies in whether a univariate or multivariate PCA is performed so that we have a different set of principal component scores for each functional time series or the same one for all of them. Finally, the sample performance of the proposed methodologies is illustrated by an application on a bivariate functional time series of reset/set curves.