Trends In Time Series Research Articles

The Rise of AI Pricing: Trends, Driving Forces, and Implications for Firm Performance

We document key stylized facts about the time-series trends and cross-sectional distributions of AI pricing and study its implications for firm performance, both on average and conditional on monetary policy shocks. We use the universe of online job posting data from Lightcast to measure the adoption of AI pricing. We infer that a firm is adopting AI pricing if it posts a job opening that requires AI-related skills and contains the keyword “pricing.” At the aggregate level, the share of AI-pricing jobs in all pricing jobs has increased by more than tenfold since 2010. The increase in AI-pricing jobs has been broad-based, spreading to more industries than other types of AI jobs. At the firm level, larger and more productive firms are more likely to adopt AI pricing. Moreover, firms that adopted AI pricing experienced faster growth in sales, employment, assets, and markups, and their stock returns are also more sensitive to high-frequency monetary policy surprises than non-adopters. We show that these empirical observations can be rationalized by a simple model where a monopolist firm with incomplete information about the demand function invests in AI pricing to acquire information.

Persistent Behavior in Energetic Neutral Atom Time Series from IBEX

Abstract We investigate the long-term persistence of the time series of energetic neutral atom (ENA) fluxes recorded by the Interstellar Boundary Explorer (IBEX). ENAs provide global information about the outer heliosphere and its interactions with the very local interstellar medium. To avoid the IBEX Ribbon, here, we focus our analysis solely on the polar regions N60°–N90° and S60°–S90°. Each year, IBEX takes two measurements of every pixel in the sky. We make use of the whole set of 14 yr of IBEX data and adhere to the correct time order for the construction of the flux time series. We examine in detail both the trend and the fluctuations of these time series. Using modern methods of time series analysis and persistence characterization, we show that the time series (i) trend is influenced by the solar cycle, (ii) persistence can be established independently of the presence of this trend, and (iii) statistical properties of the fluctuations differs between north and south, pointing to the existence of anisotropy and thus a north–south asymmetry.

Time series trends and correlations of aerosol optical depth and cloud parameters over Addis Ababa

Aerosols are microscopic liquid or solid particles that enter the atmosphere through natural and man-made processes. Time series trends and correlations of aerosol optical depth and cloud parameters over Addis Ababa have become the subject of scientific research because of the importance of clouds in controlling climate. In this study, we have examined the temporal variations (time series analysis) in aerosol particles over Addis Ababa and the impact of these variations on various optical properties of clouds using Moderate Resolution Imaging Spectroradiometer (MODIS) data from January 2003 to December 2018. The maximum aerosol optical depth (AOD) was found during the summer period, with minimum values in winter. During summer, the air temperature is high, which leads to abundant atmospheric water vapor, facilitating the hygroscopic growth of aerosols. We then analyzed the relationships between AOD and other cloud parameters, namely, water vapor (WV), cloud fraction (CF), cloud top temperature (CTT), and cloud top pressure (CTP), to provide a better understanding of aerosol–cloud interactions. The correlation between AOD and CF and WV was positive for Addis Ababa, while there was a negative correlation with CTT and CTP.

Making medical education great again: analysing trainee feedback to identify key drivers of trainee satisfaction and improve experience

ObjectivesThe London School of Paediatrics (LSP) surveys trainees annually regarding their training placements. There is a wide variation in satisfaction. We explored the last 5 years of data to identify trends and determine predictors of overall trainee satisfaction.MethodsThe survey is distributed annually to all London paediatric trainees with response rates of 86–90%. It explores overall placement satisfaction, followed by key questions covering staffing, supervision, teaching, facilities and other facets. The survey blends both quantitative and qualitative feedback.Satisfaction levels were analysed using time series trend analysis. Overall levels were compared using t-tests. Associations between satisfaction and components of the survey were identified using a convergence of multiple regression and other multivariate analyses.ResultsOverall trainee satisfaction increased steadily until 2020. There was a considerable variation across sites, some having consistently higher or lower scores than the median, while others showed an upward or downward trend, or very variable pattern.By training levels, ST3 trainees consistently rated their placement below average and ST7 trainees above average. There was also a considerable variation in responses by training level with regard to perceived staffing levels and teaching.To understand the influences on overall satisfaction, regression coefficients were derived between specific metrics and overall satisfaction using multiple regression and then normalised to 100%. The explanatory power of this list to explain satisfaction overall is high at 82% R2.Trainee satisfaction was most strongly correlated with workplace atmosphere and consultant support. Staffing, educational supervision, facilities and wider MDT (Multidisciplinary Team) support are associated with overall satisfaction, but to a lesser degree.ConclusionsTrainee satisfaction is crucial, affecting patient safety, retention and trainee mental health. Our analysis identified significant associations with satisfaction including modifiable elements such as consultant engagement and team morale. Staffing levels were less related to satisfaction than expected.Experimental data exploring the components of clinical training in real-world situations are lacking. These data use the natural experiment of placing trainees in different training locations to explore the factors that relate to their overall experience.

Patterns of alcohol use in Belgium, 2013-2020

Abstract Despite numerous efforts worldwide to reduce the harmful effects of alcohol, its impact on health remains significant, contributing significantly to the loss of healthy life years. In Belgium, alcohol use is still a major public health concern, ranking among the highest in alcohol consumption across Europe. This study aims to use available nationally representative data to estimate and evaluate the time trends of alcohol use patterns in Belgium from 2013 to 2018. We used repeated cross-sectional survey data from the Belgian Health Interview Survey (BHIS), which includes self-reported alcohol use and sociodemographic data, to create a time series of annual consumption from 2013 to 2018. This included current and former drinkers, and lifetime abstainers by year, sex, region, and age groups. We applied a Bayesian hierarchical model, using the Integrated Nested Laplace Approximation to create the time series and forecast trends for 2020. In Belgium, the prevalence of current drinking remained at a similar level of 80% from 2013 to 2020, with similar rates observed in the Flemish (FL) and Walloon (WA) regions. However, the Brussels-Capital region (BR) had a slightly lower prevalence of 70%. Trends remained relatively stable across all regions during this period. The national level of former drinkers increased from 5% in 2013 to 8% in 2020, with BR and FL showing similar increases, while WA experienced a decline. Abstainer rates rose from 15% in 2013 to 19% in 2020 nationally, with varying increases in each region. Specifically, abstainer rates in FL increased from 13% to 17% and in WA from 14% to 20%, while BR saw an increase from 19% to 22%. Overall, declining trends were evident, with men exhibiting higher prevalence rates across all categories. Time trends from the BHIS reveal consistent and high use of alcohol in Belgium over 7 years, emphasizing the ongoing public health challenge. Urgent policy interventions are needed to reduce the impact of alcohol on health Key messages • Alcohol use in Belgium is still a pressing public health issue, with high prevalence rates of current drinking in the population. Different patterns of alcohol use can be seen between regions. • Understanding the role of alcohol on population health as an attributable burden can improve health monitoring.

Does Google Analytics Improve the Prediction of Tourism Demand Recovery?

Research shows that Google Trend indices can improve tourism-demand forecasts. Given the impact of the recent pandemic, this may prove to be an important predictor of tourism recovery in countries that are still struggling to recover, including South Africa. The purpose of this paper is firstly, to build on previous research that indicates that Google Trends improves tourism-demand forecasting by testing this within the context of tourism recovery. Secondly, this paper extends previous research by not only including Google Trends in time-series forecasting models but also typical tourism-demand covariates in an econometric specification. Finally, we test the performance of Google Trends in forecasting over a longer time period, because the destination country is a long-haul destination where more lead time may be required in decision-making. Additionally, this research contributes to the body of knowledge by including lower frequency data (quarterly) instead of the higher frequency data commonly used in current research, while also focusing on an important destination country in Africa. Due to the differing data frequencies, the MIDAS modelling approach is used. The MIDAS models are compared to typical time-series and naïve benchmarks. The findings show that monthly Google Trends improve forecasts on lower frequency data. Furthermore, forecasts that include Google Trends are more effective in forecasting one to two quarters ahead, pre-COVID. This trend changed after COVID, when Google Trends led to improved recovery forecasts even over a longer term.

A study of short-term wind power segmentation forecasting method considering weather on ramp segments

The short-term fluctuation of wind power can affect its prediction accuracy. Thus, a short-term segmentation prediction method of wind power based on ramp segment division is proposed. A time-series trend extraction method based on moving average iteration is proposed on the full-time period to analyze the real-time change characteristics of power time-series initially; secondly, a ramp segment extraction method based on its definition and identification technique is proposed based on the results of the trend extraction; and a segmentation prediction scheme is proposed to lean the power prediction under different time-series: the LightGBM-LSTM is proposed for the non-ramping segment using point prediction, and the CNN-BiGRU-KDE is proposed for probabilistic prediction of ramp segments. From the results, this ramp segment definition and identification technique can effectively identify the ramp process of wind power, which makes up for the misidentification and omission of the classical climbing event definition; meanwhile, the segment prediction scheme not only meets the prediction accuracy requirements of the non-ramping segment, but also provides the effective robust information for the prediction of the ramping period, which offers reliable reference information for the actual wind farms. In particular, it is well adapted to wind power prediction under extreme working conditions caused by ramping weather, which is a useful addition to short-term wind power prediction research.

Fault Detection in Industrial Equipment through Analysis of Time Series Stationarity

Predictive maintenance has gained importance due to industrialization. Harnessing advanced technologies like sensors and data analytics enables proactive interventions, preventing unplanned downtime, reducing costs, and enhancing workplace safety. They play a crucial role in optimizing industrial operations, ensuring the efficiency, reliability, and longevity of equipment, which have become increasingly vital in the context of industrialization. The analysis of time series’ stationarity is a powerful and agnostic approach to studying variations and trends that may indicate imminent failures in equipment, thus contributing to the effectiveness of predictive maintenance in industrial environments. The present paper explores the use of the Augmented Dickey–Fuller p-value temporal variation as a possible method for determining trends in sensor time series and thus anticipating possible failures of a wood chip pump in the paper industry.

Oberved trend of precipitation extreme in Kalimantan

<p style="line-height:200%;margin-bottom:.0001pt;text-align:justify;text-justify:inter-ideograph;"><span style="font-family:"Times New Roman";" lang="IN">It has been suggested that the global warming due to an increase in green house gas concentrations could lead to extreme climate events, such as </span><span style="color:#007BB8;font-family:"Times New Roman";" lang="IN">precipitation extreme and droughts</span><span style="font-family:"Times New Roman";" lang="IN">. This study is designed to analyze trend in the long-term time series of preciptation extreme in Kalimantan, focusing on observed data obtained from eight meteorological stations spanning from January 1985 to December 2022. </span><span style="background-color:white;color:#0D0D0D;font-family:"Times New Roman";" lang="EN-ID">Statistical analyses were employed to discern patterns and trends in extreme precipitation events. The findings reveal a discernible long-term trend characterized by fluctuations in precipitation extremes over the study period. The trend of total annual precipitation (PRCPTOT) has increased at all observation stations. Especially at SM. Nangapinoh and Supadio, the PRCPTOT trend increased significantly at the 99% and 90% levels. Meanwhile, the increase in consecutive wet days (CWD) trends only occurred at SM. Nangapinoh and Rahadi Oesman (West Kalimantan), and SM. Iskandar (South Kalimantan). A significant trend was found in SM. Nangapinoh (90%) and Iskandar (95%). In addition to experiencing an increase in the R20mm trend, there was a decrease in the trend. Significant trend increases at the 99% and 90% levels occurred in SM. Nangapinoh and Supadio.  In addition, an increase in CDD trend occurred only in SM. Rahadi Oesman and Tjilik Riwut, although not significant. Meanwhile, the majority of extreme precipitation indices have a greater correlation with ENSO than the IOD. </span></p>

Intersensor Calibration of Spaceborne Passive Microwave Radiometers and Algorithm Tuning for Long-Term Sea Ice Trend Analysis Based on AMSR-E Observations

Sea ice monitoring is key to analyzing the Earth’s climate system. Long-term sea ice extent (SIE) has been continuously monitored using various spaceborne passive microwave radiometers (PMRs) since November 1978. As the lifetime of a satellite is usually approximately 5 years, bias caused by differences in PMRs should be eliminated to obtain objective SIE trends. Most sea ice products have been analyzed for long-term trends with a bias adjustment based on the coarse resolution special sensor microwave imager (SSM/I) in operation for the longest period. However, since 2002, Japanese microwave radiometers of the Advanced Microwave Scanning Radiometer (AMSR) series, which have the highest spatial resolution in PMR, have been available. In this study, we developed standardization techniques for processing SIE including calibration of the brightness temperature (TB), tuning the sea ice concentration (SIC) algorithm, and adjusting the SIC threshold to retrieve a consistent SIE trend based on the AMSR for the Earth Observing System (AMSR-E, one of the AMSR that operated from May 2002 to October 2011). Analysis results showed that the root-mean-square error between AMSR-E SICs and those of moderate resolution imaging spectroradiometer (MODIS) was 15%. In this study, SIE was defined as the sum of the areas where the AMSR-E SIC was >15%. When retrieving SIE, we adjusted the SIC threshold for each PMR to be consistent with the SIE calculated based on the 15% SIC threshold for AMSR-E. We then calculated a time-series of the SIE trends over approximately 45 years using the adjusted SIE data. Therefore, we revealed the dramatic decrease in global sea ice extent since 1978. This technique enables retrieval of more accurate long-term sea ice trends for more than half a century in the future.

Disparities in Syphilis Trends and the Impact of the COVID-19 Pandemic: A Joinpoint Analysis of Florida Surveillance Data (2013-2022).

Syphilis, a sexually transmitted infection caused by the bacterium​​​​​​Treponema pallidum, has reemerged at an increasing rate globally in recent years, posing a significant public health concern. Visualizing and analyzing time series trends of syphilis across various demographics and geographic regions, and how syphilis trends varied during the pandemic coronavirus disease 2019 (COVID-19), would help public health policymakers develop targeted strategies and interventions to effectively address the rising rates of syphilis, ultimately improving overall public health outcomes. A retrospective study was conducted using surveillance data on infectious syphilis cases reported to the Florida Department of Health, between 2013 and 2022. Age-standardized syphilis rates (ASSRs) per 100,000 persons were calculated using the 2000 U.S. standard population data. The average annual percentage change (AAPC) in the ASSRs was analyzed according to age, gender, race, geographic locations, and the COVID-19 pandemic period to quantify the epidemiological syphilis trends using joinpoint regression models. In Florida, the ASSR significantly increased from 8.65/100,000 in 2013 to 23.08/100,000 in 2022 across different demographic groups, with an overall AAPCof 11.47 (95% confidence interval (CI): 9.85-13.43). This increase in the AAPC was more pronounced in females (AAPC= 20.97; 95% CI, 18.61-24.49) than in males (AAPC = 10.34; 95% CI, 8.19-12.98). The increasing trends of infectious syphilis were observed across all age groups, with greater increases among those aged 25-49 years (AAPC = 12.32; 95% CI, 10.09-15.18), aged 50-69 years (AAPC= 13.42; 95% CI, 9.41-18.89), and aged over 70 years (AAPC= 13.63; 95% CI, 9.23-21.95), compared to those aged less than 24 years (AAPC= 7.86; 95% CI, 7.06-8.81). The increasing trends were comparable across racial groups, with an AAPC of 8.08 (95% CI, 5.47-11.15) for Hispanics, 11.84 (95% CI, 10.02-14.09) for non-Hispanic Whites, 10.49 (95% CI, 8.75-12.66) for non-Hispanic Blacks, and 11.29 (95% CI, 5.28-19.57) for non-Hispanic individuals of other races, respectively. The AAPCfor the COVID-19 pandemic period was 12.99 (95% CI, 8.48-16.21), which was comparable to the pre-pandemic period (AAPC= 11.58; 95% CI, 10.17-12.76), indicating that this upward trend in infectious syphilispersisted throughout the COVID-19 pandemic. At the county level, over the 10-year period, the average ASSRs ranged from 0.89/100,000 in 2013 to 25.41/100,000 in 2022, with the estimated AAPCs varied from 2.47 (95% CI, -1.89 to 6.92) in Monroe County to 50.01 (95% CI, 37.71-62.27) in Okaloosa County. The trend of infectious syphilis increases substantially from 2013 to 2022, with notable differences observed among age, gender, and geographic regions in Florida. Females experienced a greater rise in infectious syphilis cases compared to males. This upward trend in infectious syphilispersisted throughout the COVID-19 pandemic. Efforts are needed to implement strategies to address the rising syphilis rates within high-incidence groups and communities across the state.

Clip versus coil shift for aneurysm treatment in Brazil: an exploratory analysis of trends in a 10-year time-series.

Regarding intracranial aneurysm treatment, the clip versus coil debate remains inconclusive and lacking studies in Brazil. To examine trends in the management of intracranial aneurysms in Brazil over time, both ruptured and unruptured. A descriptive and exploratory study was conducted based on data of neurovascular procedures for aneurysm treatment using the Brazilian Public Health System database (DATASUS). The variables analyzed were the number of procedures, mortality rates, length of hospital stays, and global costs of hospitalization, from 2010 to 2019. Temporal trend analysis and statistical comparisons were conducted to assess changes over time and differences between the treatment options. The mean annual number of aneurysm treatments with endovascular embolization was 2206.30 (± 309.5), with a non-significant increasing trend (B = 55.66; p = 0.104). Conversely, microsurgical clipping exhibited a significant decreasing trend (B = -69.97; p < 0.001) with a mean of 1133.1 (± 223.12) procedures. The mortality rate associated with clipping procedure was higher in the period, with a mean difference of 5.23 (± 0.39); ([CI95%: 4.36; 6.10]; p < 0.001) and showed an increase trend, while embolization showed a stable trend. The length of in-hospital stay remained stable for clipping but increased for embolization. Costs associated with clipping increased over time, whereas costs for embolization decreased. This study highlights a significant shift in the treatment of aneurysm towards Endovascular Embolization. Despite higher costs, endovascular procedures were associated with lower mortality rates and shorter hospital stays. These findings provide valuable insights into aneurysm treatment patterns and indicators in a middle-income country's Public Health System.

Surface water dynamics of Lake Chad Basin (Sahelian Africa) based on daily temporal resolution Earth observation time series

ABSTRACT Water availability is vital for the sustenance of livelihoods in the Lake Chad Basin. However, the daily and seasonal dynamics of open water bodies are not well understood. This study aims to (1) analyze the daily and seasonal dynamics of water bodies, (2) estimate changes in surface water area extent including trends and change points, and (3) assess the connection between surface water extent and seasonal rainfall variation. To achieve this, we used the Global WaterPack and ERA5-Land daily aggregated datasets. We employed time series decomposition, trends analysis, and temporal lag correlation in our analysis. The results showed strong seasonal patterns of natural lakes compared to reservoirs/dams. Between 2003 and 2022, Lake Chad averaged 2,475.64 km2. The Northern pool of Lake Chad exhibited significant fluctuations, remaining below 600 km² between 2005 and 2012, from 2016 to 2019), with less than 350 km2 lasting only for a few days annually. The Southern pool averaged between 2,200 and 2,400 km2, except during drought years (2006–2007), specifically between the days of the year to approximately 66, and days 301–365/6. In Lake Fitri, the yearly maximum and minimum water extents were observed between days 1–59 and 305–365/6, and between days 60 and 304, respectively.

Rainfall Trend Analysis in Mysuru and Bengaluru Districts of Karnataka, India

Climate change is one of the most pressing challenges facing humanity today, with temperature, precipitation, and runoff being key indicators closely linked to its impact. Understanding the spatial variability and temporal trends of rainfall is crucial for effective water resource management and agriculture. This study focuses on analyzing rainfall data from the Mysuru and Bengaluru districts, located in the Southern and Eastern dry zones of Karnataka, respectively. These regions play a pivotal role in the state's agricultural development and overall economic growth. We utilized daily precipitation records from two meteorological stations, spanning the period from 1983 to 2018, to analyze rainfall trends on monthly, seasonal, and annual scales. To assess these trends, we applied both parametric and non-parametric statistical methods, including the simple regression method, Mann-Kendall test, and Sen’s slope estimator. The Mann-Kendall test was used to detect significant trends in the precipitation time series, while Sen’s slope estimator quantified the magnitude of these trends. Bengaluru had higher average annual rainfall (928.81 mm) compared to Mysuru (681.65 mm), with excess rainfall in 2005 and a positive Pre-Monsoon trend (+3.543 mm/year), while Mysuru experienced maximum rainfall deficit in 2016 and a negative Monsoon trend (-3.238 mm/year). Neither district showed a significant trend in annual rainfall. The findings of this trend analysis are essential for informed water resource planning and management in the region.

Boosting the HP filter for trending time series with long-range dependence

This article extends recent asymptotic theory developed for the Hodrick Prescott (HP) filter and boosted HP (bHP) filter to long-range dependent time series that have fractional Brownian motion (fBM) limit processes after suitable standardization. Under general conditions, it is shown that the asymptotic form of the HP filter is a smooth curve, analogous to the finding in Phillips and Jin for integrated time series and series with deterministic drifts. Boosting the filter using the iterative procedure suggested in Phillips and Shi leads under well-defined rate conditions to a consistent estimate of the fBM limit process or the fBM limit process with an accompanying deterministic drift when that is present. A stopping criterion is used to automate the boosting algorithm, giving a data-determined method for practical implementation. The theory is illustrated in simulations and two real data examples that highlight the differences between simple HP filtering and the use of boosting. The analysis is assisted by employing a uniformly and almost surely convergent trigonometric series representation of fBM.

Long-term trends in cycle threshold values: a comprehensive analysis of COVID-19 dynamics, viral load, and reproduction number in South Korea.

With the emergence of COVID-19 cases, governments quickly responded with aggressive testing, contact tracing, isolation and quarantine measures. South Korea's testing strategy primarily relied on real-time reverse-transcriptase polymerase chain reaction (real-time RT-PCR), focusing on cycle threshold (Ct) values, indicative of viral load, to determine COVID-19 positivity. This study examined the long-term time series distribution of Ct values measured in the same laboratory using a nationally standardized testing type and sampling method in South Korea. It aimed to link Ct values, new COVID-19 cases, and the reproduction number (Rt), setting the stage for using Ct values effectively. This study analyzed nationally collected 296,347 samples Ct values from February 2020 to January 2022 and examined their associations with the number of new cases and Rt trends. The data were categorized into four COVID-19 periods for in-depth analysis. Statistical methods included time series trend analysis, local regression for smoothing, linear regression for association analysis, and calculation of correlation coefficients. The median Ct values across four COVID-19 periods decreased gradually from 31.71 in the initial period to 21.27 in the fourth period, indicating higher viral load. The comparison of trends between Ct values and the number of new cases revealed that the decline in Ct values preceded the surge in new cases, particularly evident during the initial stages when new cases did not undergo a significant increase. Also, during variant emergence and vaccination rollout, marked shifts in Ct values were observed. Results from linear regression analysis revealed a significant negative relationship between Ct values and new cases (β = -0.33, p < 0.001, R 2 = 0.67). This implies that as Ct values decrease, new case numbers increase. This study demonstrates the potential of Ct values as early indicators for predicting confirmed COVID-19 cases during the initial stages of the epidemic and suggests their relevance in large-scale epidemic monitoring, even when case numbers are similar.

Long-term trend forecast of chlorophyll-a concentration over eutrophic lakes based on time series decomposition and deep learning algorithm

Long-term trend forecast of chlorophyll-a concentration (Chla) holds significant implications for eutrophication management and pollution control planning on lakes, especially under the background of climate change. However, it is a challenging task due to the mixture of trend, seasonal and residual components in time series and the nonlinear relationships between Chla and the hydro-environmental factors. Here we developed a hybrid approach for long-term trend forecast of Chla in lakes, taking the Lake Taihu as an instantiation case, by the integration of Seasonal and Trend decomposition using Loess (STL), wavelet coherence, and Convolutional Neural Network with Bidirectional Long Short-Term Memory (CNN-BiLSTM). The results showed that long-term trends of Chla and the hydro-environmental factors could be effectively separated from the seasonal and residual terms by STL method, thereby enhancing the characterization of long-term variation. The resonance pattern and time lag between Chla and the hydro-environmental factors in the time-frequency domain were accurately identified by wavelet coherence. Chla responded quickly to variations in TP, but showed a time lag response to variations in WT in Lake Taihu. The forecasting method using multivariate and CNN-BiLSTM largely outperformed the other methods for Lake Taihu with regards to R2, RMSE, IOA and peak capture capability, owning to the combination of CNN for extracting local features and the integration of bidirectional propagation mechanism for the acquisition of higher-level features. The proposed hybrid deep learning approach offers an effective solution for the long-term trend forecast of algal blooms in eutrophic lakes and is capable of addressing the complex attributes of hydro-environmental data.

TIME SERIES MODELLING OF ROAD TRAFFIC ACCIDENTS IN WEST ARSI, ETHIOPIA

The aim of this research is time series modeling of road traffic accidents in the west Arsi zone, Ethiopia, it focused on monthly traffic accidents from January 2016 to December 2020. The goal of this study was to explore the number of traffic accidents to fit a time series model for the monthly number of road traffic accidents and to forecast a tow year ahead of the number of road traffic accidents. The analysis was done by using statistical software packages using this software and knowledge of time series analysis, trend, ACF, PACF, and Box-Jenkins analysis were computed. From the trend plot, the road traffic accidents was fluctuate from month to month as well as from year to year. There was total accident fluctuation from month to month (not stationary) a total of 1010 RTA were observed. The mean of 16.83 and standard deviation of 5.764 for the total accident was served the minimum and maximum record of road traffic accidents is 4 and 33 respectively. By differencing data one time, (2, 1, 3) model was fitted for making a two-year ahead forecast. Proper model adequacy checking was done. Two-year ahead forecasts showed that October, January, and April 2021 are the months with the most prominent values. Even if the trend in total accidents was decreasing there is still a need to pay more attention in order to prevent the occurrence of accidents related to road traffic accidents.

Short-term wind speed prediction based on time series trends and periodic characteristics

Short-term wind speed prediction based on time series trends and periodic characteristics

Temporal and Spatial Analysis for Precipitation Trend and Homogeneity in Semi-Arid Area, Northwestern Iraq as a Case Study

Detecting the trends and homogeneity in precipitation records is fundamental in climate change and hydrological studies. This study investigates the homogeneity and trends of precipitation time series at four stations in north-west Iraq. The analysis encompasses the annual, seasonal, and monthly time scale at a significant level of 5%. Four tests were employed to assess the homogeneity analysis of the precipitation time series: Standard Normal Homogeneity, Buishand, Pettitt, and Von Neumann Ratio. Then two methods were applied to detect the trend from 1970–2020. Sequential Mann- Kendall and Innovative trend tests were employed to track the significant trend changes and to find the magnitude, tendency, and significance of the trend during this period for precipitation. Microsoft Excel was utilized to achieve the concepts of homogeneity and trend for the precipitation time series. The results of the homogeneity test confirmed the suitability and homogeneity of the precipitation for further analysis. The trend analysis showed that there is an annual increasing (positive) trend in precipitation at Duhok and Rabia stations by 14 mm/decade and 0.25 mm/decade, respectively. Conversely, Mosul and Tal-Afer stations exhibited a decreasing (negative) trend by 21 mm/decade and 8.9 mm/decade, respectively. Spring precipitation exhibited a statistically significant decreasing trend in all Northwestern stations. The most substantial value in the Mosul station by 11mm/decade. This study highlights the importance of investigating drought and flood adaptation strategies to address the impact of climate changes impacts at the mentioned area.