Time Series Analysis Techniques Research Articles

Admission Prediction Using Time Series Analysis

Efficient resource allocation and strategic planning in educational institutions heavily rely on accurate predictions of student admissions. This paper presents a detailed investigation into the application of time series analysis techniques for admission prediction. We explore the utilization of historical admission data, spanning multiple academic cycles, to train and evaluate several time series models.The study encompasses a comparative analysis of traditional statistical models such as autoregressive integrated moving average (ARIMA) and seasonal autoregressive integrated moving average (SARIMA) alongside more advanced deep learning techniques like long short-term memory (LSTM) networks. By evaluating the performance metrics, including accuracy, robustness, and computational efficiency, we aim to identify the most suitable model for admission prediction tasks. Moreover, the research delves into the impact of various external factors such as changes in the academic calendar, socio-economic indicators, and demographic shifts on admission patterns. Understanding these factors is crucial for enhancing the predictive capabilities of the models and enabling institutions to adapt their strategies accordingly. The experimental results and comparative analysis provide valuable insights for educational institutions, enabling them to make data-driven decisions regarding enrollment management strategies and resource allocation. Ultimately, this research contributes to the advancement of admission prediction methodologies, facilitating more efficient and informed decision-making processes in the educational domain.

A Comparative Analysis of the Level of Direct Commercial and Financial Relations: A Case Study of the Two Sides of the Taiwan Straits in the Past Ten Years

This research conducts a comprehensive analysis of the direct commercial and financial relations between Taiwan and mainland China over the past ten years, aiming to understand the depth, trends, and impacts of economic ties. The study employs a comparative analysis approach to leverage data from reputable sources such as the International Monetary Fund (IMF), World Bank, and official reports from Taiwan and China. The analysis of trade patterns reveals a consistent increase in volumes, showcasing a maturing economic relationship marked by the diversification of traded goods. Cross-Strait investments, particularly in critical sectors like technology and finance, highlight strategic collaborations and sector-specific interdependence. Financial relationships, examined through IMF reports and national financial institutions, demonstrate a deepening integration of banking ties despite geopolitical complexities. The study employs theoretical frameworks such as Complex Interdependence Theory and Political Economy Theory to interpret findings. The data analysis includes time-series analysis techniques to identify seasonality, trends, and cyclical patterns, providing insights for policymakers and businesses. The study's significance lies in its contribution to understanding the multifaceted nature of cross-Strait economic relations, emphasizing the endurance of economic interactions despite political complexities. The findings provide a nuanced foundation for improved bilateral relations, offering policymakers insights for strategic adjustments and fostering sustainable economic collaboration. In conclusion, this research contributes to the academic discourse on Taiwan-China economic relations by providing a detailed and empirically supported analysis. It addresses the intricacies of trade, investments, and financial collaborations, shedding light on the dynamics shaping the economic ties between these two entities.

Multilevel resistive switching in hydrothermally synthesized FeWO4 thin film-based memristive device for non-volatile memory application

The memristors offer significant advantages as a key element in non-volatile and brain-inspired neuromorphic systems because of their salient features such as remarkable endurance, ability to store multiple bits, fast operation speed, and extremely low energy usage. This work reports the resistive switching (RS) characteristics of the hydrothermally synthesized iron tungstate (FeWO4) based thin film memristive device. The detailed physicochemical analysis was investigated using Rietveld’s refinement, X-ray photoelectron spectroscopy (XPS), field emission scanning electron microscopy (FE-SEM), and transmission electron microscopy (TEM) techniques. The fabricated Ag/FWO/FTO memristive device exhibits bipolar resistive switching (BRS) behavior. In addition, the devices exhibit negative differential resistance (NDR) at both positive and negative bias. The charge-flux relation portrayed the non-ideal or memristive nature of the devices. The reliability in the RS process was analyzed in detail using Weibull distribution and time series analysis techniques. The device exhibits stable and multilevel endurance and retention characteristics which demonstrates the suitability of the device for the high-density non-volatile memory application. The current conduction of the device was dominated by Ohmic and trap controlled-space charge limited current (TC-SCLC) mechanisms and filamentary RS process responsible for the BRS in the device. In a nutshell, the present investigations reveal the potential use of the iron tungstate for the fabrication of memristive devices for the non-volatile memory application.

Deep learning for prediction of energy consumption: an applied use case in an office building

Abstract Non-residential buildings are responsible for more than a third of global energy consumption. Estimating building energy consumption is the first step towards identifying inefficiencies and optimizing energy management policies. This paper presents a study of Deep Learning techniques for time series analysis applied to building energy prediction with real environments. We collected multisource sensor data from an actual office building under normal operating conditions, pre-processed them, and performed a comprehensive evaluation of the accuracy of feed-forward and recurrent neural networks to predict energy consumption. The results show that memory-based architectures (LSTMs) perform better than stateless ones (MLPs) even without data aggregation (CNNs), although the lack of ample usable data in this type of problem avoids making the most of recent techniques such as sequence-to-sequence (Seq2Seq). Graphical abstract

Time Series Analysis and Neural Network Optimization of Tourist Destination Heat Prediction Model

Accurate prediction of tourist destination heat is significant for effectively managing tourist flow in the tourism industry. However, forecasting the temperature of a tourist destination is challenging, and various factors, like weather conditions, influence it. In this study, we proposed a novel hybrid strategy using time series analysis and neural network optimization to predict tourist destination heat accurately. The study commences with collecting historical tourist visitation data, weather records, and other information. Then, the raw database was preprocessed to make the data effective and reliable for subsequent analysis. Further, the Autoregressive Integrated Moving Average (ARIMA) technique was employed as a time series analysis technique, capturing and identifying the database's underlying patterns and correlations. Consequently, a Fire Hawk Optimizer-based Dense Recurrent Neural Network (FHO-DRNN) was developed and trained using the ARIMA outcomes to predict the tourist destination heat. The proposed framework was implemented in the MATLAB software, and the results are examined in terms of accuracy, Mean Absolute Error (MAE), and computational time. Furthermore, we compared the existing predictive models to validate the proposed model's effectiveness in forecasting tourist destination heat.

The virtual drum circle: polyrhythmic music interactions in mixed reality

Emerging technologies in the domain of mixed reality offer rich, new possibilities for the study and practice of joint music performance. Apart from the technological challenges, bringing music players together in mixed reality raises important questions on their performance and embodied coordination. In this study, we designed a mixed reality platform to assess a remote, bidirectional polyrhythmic interaction between two players, mediated in real time by their three-dimensional embodied avatars and a shared, virtual ‘drum circle’. We leveraged a multi-layered analysis framework to assess their performance quality, embodied coregulation and first-person interaction experience, using statistical techniques for timeseries analysis and mixed-effect regression and focusing on contrasts of visual coupling (not seeing / seeing as avatars / seeing as real) and auditory context (metronome / music). Results reveal that an auditory context with music improved the performance output as measured by a prediction error, increased movement energy and levels of experienced agency. Visual coupling impacted experiential qualities and induced prosocial effects with increased levels of partner realism resulting in increased levels of shared agency and self-other merging. Embodied coregulation between players was impacted by auditory context and visual coupling, suggesting prediction-based compensatory mechanisms to deal with the novelty, difficulty, and expressivity in the musical interaction. This study contributes to the understanding of music performance in mixed reality by using a methodological approach to demonstrate how coregulation between players is impacted by visual coupling and auditory context and provides a basis and future directions for further action-oriented research.

A Study on the Spill Over Effects on Nifty 50 Returns: Evidence from Stock Market Performances of Major Asian Counterparts of India

This study investigates the spillover effects of major Asian stock market performances on the returns of the Nifty 50, India's benchmark index. Employing advanced econometric techniques, we analyze the dynamic relationships between the Nifty 50 and key Asian counterparts, considering both short-term and long-term horizons. Our research delves into return spillovers aiming to comprehensively assess the interconnectedness and transmission of market movements across the region. This study utilizes an extensive dataset covering Nifty 50 returns from April 1, 2013, to March 31, 2023, along with meticulously documented returns from stock market indexes linked to major strategic alliances in India. By applying advanced multivariate time series analysis techniques, encompassing Vector Autoregression (VAR), Granger causality tests, and impulse response functions, we model the intricate dynamics interweaving these indexes with the Nifty 50. The research endeavors to answer fundamental questions, including assessing short- term and long-term effects resulting from major Asian counterparts of India on Nifty returns, as well as exploring noticeable patterns that indicate the direction of causality between these counterparts and Nifty returns. The outcomes of this study carry substantial implications for decision-makers. Investors can make more informed decisions, businesses planning strategic partnerships can refine their strategies, and policymakers can formulate economic and financial regulations that accurately reflect the complexities of these dynamics. By unveiling the ripple effects of these counterparts on the Nifty 50, this research offers valuable insights into the evolving dynamics of India's financial markets and their integration into the global economy.

Design of an Iterative Method for Enhanced Multimodal Time Series Analysis Using Graph Attention Networks, Variational Graph Autoencoders, and Transfer Learning

In the ever-evolving landscape of data analysis, the need to efficiently and accurately interpret multimodal time series data has become paramount. Traditional methods often fall short in addressing the complex dependencies and dynamics inherent in such data, limiting their effectiveness in real-world applications. This work introduces a comprehensive approach that leverages Graph Attention Networks (GATs), Variational Graph Autoencoders (VGAEs), transfer learning with pretrained transformers, and Bayesian state-space models to overcome these limitations. GATs are selected for their ability to dynamically focus on relevant modalities through attention mechanisms, thereby capturing the intricate relationships between different data modalities. This method significantly enhances the model's ability to integrate multimodal information, leading to notable improvements in classification, prediction, and anomaly detection tasks. VGAEs are utilized to learn latent representations within a graph-based framework, promoting unsupervised learning while unveiling the underlying data structure. The resultant embeddings are pivotal for downstream tasks like clustering and visualization, encapsulating the interactions within multimodal time series data effectively. Furthermore, this work incorporates transfer learning with pretrained transformers to harness extensive knowledge from large datasets, adapting it to multimodal time series analysis. This strategy excels in capturing long-range dependencies, thereby augmenting generalization and performance in data-scarce scenarios. Bayesian state-space models are employed to elucidate the temporal dynamics and uncertainties of time series data, offering a robust framework for probabilistic inference and enhancing the interpretability and reliability of forecasting and anomaly detection. The efficacy of the proposed model is rigorously evaluated using diverse datasets, including the Yahoo! Stock Dataset, Forest Cover Dataset, and an empirical collection of 100k time series data samples. The results demonstrate a significant leap in performance metrics, including a 9.5% increase in precision, 8.5% boost in accuracy, 8.3% rise in recall, 10.4% reduction in delay, 9.4% enhancement in AUC, and a 5.9% improvement in specificity, alongside superior pre-emption capabilities compared to existing methods. This work not only addresses the pressing need for advanced multimodal time series analysis techniques but also sets a new benchmark for efficiency and accuracy. The integration of GATs, VGAEs, transfer learning with pretrained transformers, and Bayesian state-space models presents a formidable approach that significantly advances the field, offering profound impacts on a wide array of applications.

Characterizing the Solar Cycle Variability Using Nonlinear Time Series Analysis at Different Amounts of Dynamo Supercriticality: Solar Dynamo is Not Highly Supercritical

The solar dynamo is essentially a cyclic process in which the toroidal component of the magnetic field is converted into the poloidal one and vice versa. This cyclic loop is disturbed by some nonlinear and stochastic processes mainly operating in the toroidal to poloidal part. Hence, the memory of the polar field decreases in every cycle. On the other hand, the dynamo efficiency and, thus, the supercriticality of the dynamo decreases with the Sun’s age. Previous studies have shown that the memory of the polar magnetic field decreases with the increase of supercriticality of the dynamo. In this study, we employ popular techniques of time series analysis, namely, compute Higuchi’s fractal dimension, Hurst exponent, and Multi-Fractal Detrended Fluctuation Analysis to the amplitude of the solar magnetic cycle obtained from dynamo models operating at near-critical and supercritical regimes. We show that the magnetic field in the near-critical regime is governed by strong memory, less stochasticity, intermittency, and breakdown of self-similarity. On the contrary, the magnetic field in the supercritical region has less memory, strong stochasticity, and shows a good amount of self-similarity. Finally, applying the same time series analysis techniques in the reconstructed sunspot data of 85 cycles and comparing their results with that from models, we conclude that the solar dynamo is possibly operating near the critical regime and not too much supercritical regime. Thus the Sun may not be too far from the critical dynamo transition.

Empirical Approach to Modelling and Forecasting Inflation using ARIMA Model: Evidence from Tanzania

This study investigates the dynamics of the inflation rate (INFL) in Tanzania spanning from 1990 to 2021 using advanced time series analysis techniques. The dataset sourced from the World Bank online database serves as the basis for analytical exploration. Initially, the Autoregressive Integrated Moving Average (ARIMA) model is applied to understand underlying patterns in the INFL data. Transitioning to Seasonal ARIMA (SARIMA) modeling encounters challenges due to the absence of pronounced seasonality or clear trends. Unit root tests, including the Augmented Dickey-Fuller (ADF) and Phillips-Perron (PP) tests, assess the stationarity of the data. Following the identification of non-stationarity, differencing is employed to achieve stationarity. Estimation of ARIMA models (ARIMA (1,1,1) and ARIMA (2,1,1)) is conducted, with diagnostic checks confirming the suitability of the ARIMA (1,1,1) model. The study contributes to the understanding of inflation dynamics and facilitates evidence-based economic policymaking in Tanzania.

A novel paradigm: Addressing real-time decomposition challenges in carbon price prediction

Carbon price prediction serves as a market indicator and economic driver, facilitating the adoption of more environmentally friendly production methods to achieve the emission reduction targets outlined in the Paris Agreement. In recent years, time series analysis and decomposition techniques have been widely applied to carbon price forecasting. However, few researchers have considered the issue of data feature drift caused by real-time decomposition. Specifically, as the sample size increases, data features undergo changes, rendering the trained models unable to fit the new data. This paper explains the underlying reasons for this phenomenon from new perspectives and proposes a novel paradigm that replaces the intrinsic mode function with a single-step fuzzy particle. This new paradigm corrects the issue of data feature drift and concentrates noise into a completely new sequence during the preprocessing stage. In the subsequent processing steps, the loss of correlation between the sequence and time lag, as well as the sequence and carbon prices, is addressed through multi-information association. This paradigm hybrid model can be applied to deterministic and interval multi-step predictions. Experimental results on the datasets from Guangdong and Hubei demonstrate that the proposed model outperforms other comparative models, achieving better predictive results than existing decomposition-based forecasting models. In the case of Guangdong, the normalized root mean square error (NRMSE%) for one-step, three-step, and five-step deterministic predictions are 2.18%, 2.51%, and 2.89%, respectively. The average interval score (AIS) for interval predictions are −0.357 and − 0.2375, respectively.

Decreased Instability in High-Risk (Hip–Spine 2B) Patients After Modifications of Surgical Planning and Technique

BackgroundPatients undergoing primary total hip arthroplasty (THA) who have spinal deformity and a stiff spine are the highest-risk group for instability. Despite the increasing use of dual-mobility cups and large femoral heads, dislocation remains a major complication after THA. Preoperative planning becomes a critical aspect of ensuring precise component positioning within a safe zone. The purpose of this study was to investigate dislocation rates over a 9-year period. MethodsA retrospective review of 4,731 THAs performed by 3 orthopaedic surgeons between January 2014 and March 2023 was performed. Spinopelvic measurements were conducted to determine the hip–spine classification group for each patient. Only patients classified as 2B (pelvic incidence-lumbar lordosis > 10° and Δsacral slope < 10°) were eligible. Both absolute and relative dislocation frequencies were then analyzed using time-series analysis techniques and Fisher’s exact tests. ResultsA total of 281 hip–spine 2B patients undergoing primary THA were eligible for analysis (57% women; mean age, range: 66 years, 23 to 87; mean body mass index, range: 28, 16 to 45). The overall dislocation rate was 4.3%. Use of femoral head sizes ≥ 40 mm increased from 4% in 2014 to 2019 to 37% in 2020 to 2023 (P < .001), while the use of dual-mobility cups decreased from 100% in 2014 to 2019 to 37% in 2020 to 2023 (P < .001). Acetabular component planning was changed from the supine plane to the standing plane in February 2020. Those changes in surgical practice were notably correlated with a significant decrease in dislocation rates from 6.8% in 2014 to 2019 to 1.5% in 2020 to 2023 (P = .03). ConclusionsOur study demonstrates that the introduction of advanced preoperative THA planning to the standing plane, coupled with precise intraoperative technology for implant placement, can significantly reduce the risk of instability in high-risk THA patients. Notably, we observed a significant decrease in dislocation rates, which aligned with the shift in surgical practice. Level of EvidenceIV.

Interest Rate Sensitivity of Depositors in Non-Financial Banks: A Case of an emerging Economy

This study uses 62 years of data, from 1960 to 2021, including variables like deposits, interest rates, GDP, and inflation, to investigate the link between interest rates and deposit behavior in non-Islamic banks in Pakistan. The study finds that interest rates have a big influence on deposit behavior using time series analytic techniques as unit root testing, cointegration analysis, and Vector Error Correction Model (VECM). More specifically, people's decisions to deposit money in non-Islamic banks are observed to be influenced by interest rate variations. The results highlight the importance of interest rate changes in influencing deposit behavior, showing how people choose which non-Islamic institutions to deposit money with. These findings have significance for financial institutions, policymakers, and economists in general. They provide important new information about the ways in which monetary policy choices affect deposit mobilization and liquidity conditions in the banking industry. The report also proposes directions for further investigation, including the possible examination of differences in deposit behavior between demographic groupings or geographical areas. To further help understand the dynamics at work, it also suggests examining how interest rates affect deposits in various sample configurations. This research advances theoretical frameworks in the field by shedding light on the complex interactions between interest rates and deposit behavior. It also offers stakeholders practical guidance on how to deal with the effects of interest rate dynamics on deposit mobilization and banking sector liquidity.

CogniFiber: Harnessing Biocompatible and Biodegradable 1D Collagen Nanofibers for Sustainable Nonvolatile Memory and Synaptic Learning Applications.

Here, resistive switching (RS) devices are fabricated using naturally abundant, nontoxic, biocompatible, and biodegradable biomaterials. For this purpose, 1D chitosan nanofibers (NFs), collagen NFs, and chitosan-collagen NFs are synthesized by using an electrospinning technique. Among different NFs, the collagen-NFs-based device shows promising RS characteristics. In particular, the optimized Ag/collagen NFs/fluorine-doped tin oxide RS device shows a voltage-tunable analog memory behavior and good nonvolatile memory properties. Moreover, it can also mimic various biological synaptic learning properties and can be used for pattern classification applications with the help of the spiking neural network. The time series analysis technique is employed to model and predict the switching variations of the RS device. Moreover, the collagen NFs have shown good cytotoxicity and anticancer properties, suggesting excellent biocompatibility as a switching layer. The biocompatibility of collagen NFs is explored with the help of NRK-52E (Normal Rat Kidney cell line) and MCF-7 (Michigan Cancer Foundation-7 cancer cell line). Additionally, the biodegradability of the device is evaluated through a physical transient test. This work provides a vital step toward developing a biocompatible and biodegradable switching material for sustainable nonvolatile memory and neuromorphic computing applications.

The Development of Forecasting Models for Life Insurance Data by Employing Time-series Analysis and Machine Learning Technique

This article is conducted with the primary objective of investigating and comparing various forecasting models, aiming to identify the optimal model for life insurance data. For this investigation, we have employed a comprehensive dataset containing monthly direct premium data from the Thai life insurance sector, spanning from January 2003 to December 2022. Our approach involves the development of time-series models to forecast direct premiums, initially employing the SARIMAX framework. Subsequently, we have introduced an additional time-series forecasting model that incorporates SVR, collectively referred to as the SVR-SARIMAX model. The evaluation criteria used for model comparison encompass the Mean Absolute Percentage Error (MAPE), Root Mean Square Error (RMSE), and the Coefficient of Determination (R2). The results of our analysis demonstrate that the SARIMAX model outperforms both the SVR and SVR-SARIMAX models, primarily due to the linear pattern in the relationship between the independent and dependent variables. Nevertheless, it is noteworthy that the proposed SVR-SARIMAX model exhibits an improvement in prediction accuracy compared to the standalone non-linear model (SVR), even though the linear model (SARIMAX) still demonstrates superior accuracy.

Momentum portfolio selection based on learning-to-rank algorithms with heterogeneous knowledge graphs

Artificial intelligence techniques for financial time series analysis have been used to enhance momentum trading methods. However, most previous studies, which have treated stocks as independent entities, have overlooked the significance of correlations among individual stocks, thus compromising portfolio performance. To address this gap, a momentum trading framework is proposed that combines heterogeneous data, such as corporate governance factors and financial domain knowledge, to model the relationships between stocks. Our approach involves adopting a knowledge graph embedding approach to map relations among heterogeneous relationships in the data, which is then utilized to train a multitask supervised learning approach based on a learning-to-rank algorithm. This method culminates in a robust portfolio selection method on the basis of the framework. Experimental results using data from the Taiwan Stock Exchange demonstrate that our proposed method outperforms traditional linear models and other machine learning methods in predictive ability. The investment portfolio constructed serves as an invaluable aid to investment decision-making.

Two New Estimators for the Autocorrelation Function Through Singular Spectrum Analysis

It is around a century that sample autocorrelation function has been introduced and used as a standard tool in time series analysis. A vast literature can be found on the statistical properties of the sample autocorrelation function. However, it has been highlighted recently that the sum of the sample autocorrelation function over the lags 1 to [Formula: see text] is −0.5 for all time series of length T. This property produces a big concern for the cases in which all available sample autocorrelations are used in the inference. This paper provides two new alternative for estimating the autocorrelation function. These estimators come from the idea of singular spectrum analysis which is a non-parametric technique for time series analysis. The paper utilizes a simulation study to illustrate the performance of the new approach. The results suggest that further improvement to the sample autocorrelation is possible and the new methods provide an attractive alternative to the classical approach.

Spatiotemporal climate variability and trends in the Upper Gelana Watershed, northeastern highlands of Ethiopia

The aim of this study was to evaluate the performance of CHIRPS and TAMSAT satellite rainfall data over the Upper Gelana watershed, where gauged meteorological data to understand the nature of the climate are scarce. In addition, variability and trends in rainfall and temperature were examined from 1983 to 2021. To evaluate satellite rainfall, categorical and continuous validation statistics were used. Trends were analyzed using Mann-Kendall, Sen's Slope estimator, and innovative trend analysis (ITA) methods. The study also utilized time-series geostatistical analysis techniques. The validation statistics show that TAMSAT performs better on the daily timescale, while the two products have comparable performance on the monthly timescale. TAMSAT was chosen for rainfall analysis because of its higher resolution and performance. The results reveal high inter-annual spatiotemporal variability and strong irregularities in monthly rainfall. The Mann-Kendall test indicates statistically significant positive trends in kiremt and annual rainfall, but belg rainfall exhibits an insignificant negative trend. In the kiremt season, we found a 96.1, 101.6, and 104.8 mm decadal rate of rainfall increment in the lower weina dega (LWD), upper weina dega (UWD), and dega agroecological zones, respectively. In contrast, belg season rainfall declined by 16.4, 16.2, and 14.0 mm per decade in the LWD, UWD, and dega agroecology zones, respectively. The pixel-wise trend analysis also revealed trends and magnitudes of monthly, seasonal, and annual rainfall that vary across the study area. In both LWD and UWD annual minimum and maximum temperatures, respectively, showed significant decreasing and increasing trends, but in dega agroecology the trends were insignificant. The findings of rainfall and temperature trends using the ITA method demonstrated its ability to discover some hidden trends that were not detected by the MK test.

Effectiveness of Capacity-Building and Quality Improvement Interventions to Improve Day-of-Birth Care in Kinshasa, Democratic Republic of the Congo.

In sub-Saharan African settings like the Democratic Republic of the Congo, high-quality care during childbirth and the immediate postpartum period is lacking in public facilities, necessitating multipronged interventions to improve care. We used a pre-post design to examine the effectiveness of a low-dose, high-frequency capacity-building and quality improvement (QI) intervention to improve care for women and newborns around the day of birth in 16 health facilities in Kinshasa, Democratic Republic of the Congo. Effectiveness was assessed based on changes in provider skills, key health indicators, and beneficiary satisfaction. To assess changes in the competency of the 188 providers participating in the intervention, we conducted objective structured clinical examinations on care for mothers and newborns on the day of birth, immediate postpartum family planning (PPFP) counseling and method provision, and postabortion care before and after implementation of training and at 6 and 12 months after training. Interrupted time series (ITS) analysis techniques were used to analyze routine health service data for changes in select maternal, newborn, and postpartum outcomes before and after the intervention. To assess changes in clients' perceptions of care, 2 rounds of telephone surveys were administered. Before the intervention, less than 2% of participating providers demonstrated competency in skills. Immediately after training, more than 80% demonstrated competency, and 70% retained competency after 12 months. ITS analyses show the risk of early neonatal death declined significantly by 9% (95% confidence interval [CI]=4%, 13%, P<.001), and likelihood of immediate PPFP uptake increased significantly by 72% (95% CI=53%, 92%, P<.001). Client satisfaction improved by 58% over the life of the project. These findings build on previous studies documenting the effectiveness of clinical capacity-building and QI approaches. If implemented at scale, this approach has the potential to substantively contribute to improving maternal and perinatal health in similar settings.

Bitcoin Price Prediction Using Hybrid LSTM-GRU Models

Cryptocurrency price prediction is a challenging task due to the inherent volatility and complexity of the market. In this research, we propose a hybrid Long Short-Term Memory (LSTM) and Gated Recurrent Unit (GRU) neural network model for predicting Bitcoin prices. The model is implemented using the TensorFlow and Keras libraries and is evaluated on historical Bitcoin price data obtained from Yahoo Finance. Our approach aims to leverage the strengths of both LSTM and GRU architectures to enhance the accuracy of price predictions. The results suggest that the proposed hybrid LSTM-GRU model holds promise for effectively capturing the complex dynamics of cryptocurrency markets, addressing the challenges associated with traditional time-series analysis techniques.