ARIMA Research Articles

Effect of the COVID-19 pandemic on malnutrition in socially vulnerable children in Brazil.

This article aims to evaluate the effect of the COVID-19 pandemic on malnutrition among children under two years of age enrolled in the Bolsa Família Program (BFP). Ecological study of interrupted time series (ITS), with low weight for age, stunting, and overweight as time-dependent variables of malnutrition, extracted monthly (Jan/2008 to June/2021) from the Food and Nutrition Surveillance System. The COVID-19 pandemic was the exposure, dichotomized into pre-pandemic and pandemic. In RStudio, the trend was obtained by Prais-Winsten regression, and the effect of the pandemic on the time-dependent variables was determined by SARIMA modeling, estimating the regression coefficients (RC) adjusted for trend and seasonality (α = 5%). The pandemic was associated with an increase in: i) low weight for age in the South (RC = 0.94; p < 0.001) and Southeast (RC = 1.97; p < 0.001); ii) height deficit in the Midwest (RC = 2.4; p = 0.01), South (RC = 2.15; p < 0.001) and Southeast (RC = 2.96; p < 0.001); and iii) and overweight in the North (RC = 1.51; p = 0.04), Midwest (RC = 2.29; p = 0.01), South (RC = 2.83; p < 0.001), and Southeast (RC = 0.72; p = 0.04). The pandemic increased underweight in the South and Southeast, and the double burden of malnutrition in the Midwest, South, and Southeast. In the Northeast and North, higher rates of malnutrition still persist.

Exploiting the synergy of SARIMA and XGBoost for spatiotemporal earthquake time series forecasting

AbstractEarthquakes are vibrations that occur on the surface of earth, generating fires, ground shaking, tsunamis, landslides and cracks. These incidents can cause severe damage and loss of life. Accurate earthquake forecasts are critical for anticipating and mitigating these hazards, which can avoid damage to buildings and infrastructure and save lives. To address the challenges given by earthquakes probabilistic nature, this paper presents a hybrid SARIMA–XGBoost approach to earthquake magnitude prediction. The suggested technique consists of a two‐step process: an exploration phase that uses exploratory data analysis, which includes descriptive statistics and data visualisation, and a prediction phase that focusses on forecasting future earthquakes. Using a large significant earthquake dataset spanning 1965–2023, the study intends to gain insights and lessons for more effective earthquake prediction methods. Further, in a comparison analysis, the results of SARIMA‐XGBoost model are compared to those of traditional ARIMA and SARIMA models. The results highlight the superior performance of the hybrid SARIMA–XGBoost model, showcasing a mean absolute error (MAE) of 0.038, a mean squared error (MSE) of 0.0040, and a root mean squared error (RMSE) of 0.068. These metrics collectively underscore the model's enhanced accuracy in forecasting earthquake magnitudes. The notably low values of MAE, MSE and RMSE indicate that our hybrid approach significantly improves prediction accuracy compared to alternative models. By integrating SARIMA's time series (TS) analysis with XGBoost's machine learning (ML) capabilities, the hybrid model reduces forecasting errors more effectively, demonstrating its clear advantage in precision.

Modelling and forecasting mobile money customer transaction volumes in rural and semi-urban Malawi: An autoregressive integrated moving average spatial decomposition

Mobile money technologies in Malawi have revolutionised banking and monetary transactions across geographical barriers. Prospects of profit have drawn mobile money agents to invest in the business but find it is more profitable when substantial customers subscribe to the cash-in and cash-out facilities of mobile money. Despite the initial success, several challenges have emerged, including regulatory hurdles, network reliability issues, and the need for increased financial literacy among users. The volumes of transactions in the rural areas are observably lower compared to urban areas. This study uses Bvumbwe township in Malawi to model and forecast the discrepancy of mobile money transactions in rural and semi-urban Malawi. The study uses ARIMA modelling to understand the temporal manifestation of mobile money subscription in these localities. Using ARMA (1,1) models decomposed for the semi-urban and rural area, the study finds that the semi-urban area has a disproportionately higher and lasting volume of mobile money transactions compared to the rural area. The study also finds that mobile money transactions are more susceptible to long-lasting effects of external shocks in the rural area compared to the urban area. Intuitively, the day-to-day relationship in the transactions is also stronger in the rural area. These findings highlight the need for tailored policy interventions to enhance mobile money adoption and utilization in different geographical contexts.

Analysis and forecast of emissions associated with electricity consumption in hospitals: Encouraging a net zero healthcare roadmap in Brazil

In several countries, the concept of Net Zero Healthcare (NZH) has gained prominence within the healthcare domain as a response to national commitments on climate change. NZH plays a crucial role in advocating for customized commitments and objectives to reduce greenhouse gas (GHG) emissions across diverse healthcare units, serving as a catalyst for decarbonization strategies in both the public and private sectors. This paper addresses the absence of primary research on NZH in Brazil while asserting its substantial potential to support GHG-curbing initiatives. This is the first study to establish a foundational knowledge base for decarbonization initiatives in Brazilian healthcare sector, focused on assisting in the sustainability roadmap of a University Hospital situated in Northeast Brazil. The major contribution of this study is the quantification of the GHG emissions associated with the electricity consumption at the hospital, which serves as a gateway to a comprehensive GHG inventory of the entire unit. The hospital's GHG emissions are then registered for 2017-2023. Methodologically, this study employs a comprehensive framework encompassing data processing of the electric mix, calculation of emission factors via life cycle assessment, forecasting algorithms, and data visualization. GHG emissions from electricity consumption at the hospital amounted to approximately 230 t CO2e annually (2017-2023 average value). Short-term forecasts supported by SARIMA models projected emissions of 271.9 t CO2e in 2024, with 92% originating from off-peak consumption. The significance of this research is the potential to provide new insights into shaping strategies for a viable NZH roadmap in Brazil.

Predicting NEPSE Index: An ARIMA-Based Model

Purpose: This research aims to employ the Auto Regressive Integrated Moving Average (ARIMA) approach to forecast the Nepal Stock Exchange (NEPSE) index.Methods: Daily univariate time series data were taken from the NEPSE website and other sources for 27 years for a total 6143 daily time series data starting in 1997. The presence of unit roots was identified through an Augmented Dickey-Fuller (ADF) test, which ensures stationarity in data. Thereafter, estimation and evaluation of AR (autoregressive), MA (moving average), and ARIMA models were carried out.Results: The study reveals that the ARIMA (2,1,1) model is well fitted in predicting the NEPSE index, indicating its ability to capture temporal dependence and trends that exist within the data.Conclusion: It concludes the temporal dynamics of series highlighting both the short-term impacts of past shocks and longer-term adjustment towards equilibrium. The research work supports the efficacy of model in capturing and predicting the behaviour of NEPSE index, thereby adding in informed decision making and future projections based on proposed statistical foundations.

Crop cover identification based on different vegetation indices by using machine learning algorithms

In this article, three different indices NDVI (Normalized Difference Vegetation Index), BNDVI (Blue Normalized Difference Vegetation Index) and GNDVI (Green Normalized Difference Vegetation Index) are used for the identification of wheat, mustard and sugarcane crop of Saharanpur district’s region of Uttar Pradesh. Sentinel 2B satellite images are collected from October 02, 2018 to April 15, 2019. These images are processed using Google Earth Engine. These sentinel images are used to generate NDVI, BNDVI and GNDVI images using GEE. These three different indices images are further processed using SNAP software and particular indices values for 210 different locations are calculated. The same process is used for calculating BNDVI and GNDVI values. ARIMA, LSTM and Prophet models are used to train the time series indices values (NDVI, BNDVI and GNDVI) of wheat, mustard and sugarcane crop. these models are used to analyse MSE (mean absolute percentage error) and RMSE values by considering various parameters. Using ARIMA Model, for wheat crop GNDVI indices shows minimum RMSE 0.020, For Sugarcane crop NDVI indices shows minimum RMSE 0.053, For Mustard crop GNDVI indices shows minimum RMSE 0.024. Using LSTM model, for wheat crop NDVI indices shows minimum RMSE 0.036, For Sugarcane crop BNDVI indices shows minimum RMSE 0.054, For Mustard crop GNDVI indices shows minimum RMSE 0.026. Using Prophet model, for wheat crop GNDVI indices shows minimum RMSE 0.055, For Sugarcane crop NDVI indices shows minimum RMSE 0.088, For Mustard crop GNDVI indices using Prophet model shows minimum RMSE 0.101.

Time series data analysis to forecast the percentage of on-time graduates at Nusa Cendana university

The existence of information related to the estimated percentage of students graduating on time can certainly be one of the study materials that stakeholders can use to take strategies to increase the percentage of on-time graduates. One way that can be done is to use time series data related to the percentage of on-time graduates in the past year to predict the percentage of on-time graduates in the future. Forecasting can be done using ARIMA modeling. In addition, so that the solution used is also right on target, it is necessary to find out the factors that affect the study period of Undana students. The type of research used is mixed method research with sequential explanatory method where, in the first stage of research using quantitative methods, namely collecting data and quantitative analysis and in the second stage collecting and analyzing qualitative data. The quantitative data used is the percentage of on-time graduates at Nusa Cendana University in the last 5 years, while the qualitative data is the results of interviews with graduates of Nusa Cendana University related to factors that affect the length of study. The results showed that the best model for forecasting the percentage of on-time graduates of undergraduate students at Nusa Cendana University is the ARIMA (2, 2, 1) model with an AIC value of -125.64. The results of forecasting the percentage of on-time for the February, June, and September graduation periods in the next 5 years, namely 2024 to 2028, are 5.31%, 9.84%, 12.79%, 9.05%, 13.08%, 15.91%, 12.74%, 16.35%, 19.06%, 16.39%, 19.63%, 22.22%, 20%, 22.93%, and 25.41% respectively. Meanwhile, the factors that influence the study period of students are the curriculum applied in the study program, facilities and infrastructure, lecturers, and students themselves.

Evaluating Volatility Using an ANFIS Model for Financial Time Series Prediction

This paper develops and implements an Autoregressive Integrated Moving Average model with an Adaptive Neuro-Fuzzy Inference System (ARIMA-ANFIS) for BTCUSD price prediction and risk assessment. The goal of these forecasts is to identify patterns from past data and achieve an understanding of the future behavior of the price and its volatility. The proposed ARIMA-ANFIS model is compared with a benchmark ARIMA-GARCH model. To evaluated the adequacy of the models in terms of risk assessment, we compare the confidence intervals of the price and accuracy measures for the testing sample. Additionally, we implement the diebold and Mariano test to compare the accuracy of the two volatility forecasts. The results revealed that each volatility model focuses on different aspects of the data dynamics. The ANFIS model, while effective in certain scenarios, may expose one to unexpected risks due to its underestimation of volatility during turbulent periods. On the other hand, the GARCH(1,1) model, by producing higher volatility estimates, may lead to excessive caution, potentially reducing returns.

Top-up Frequency Forecasting for Lao Telecommunication Company Using Time-Series Analysis

The study aimed to forecast the top-up frequency for Lao Telecommunication Company (LTC) customers using time-series analysis. Accurate forecasts help the company optimize resource allocation, improve customer service, and boost revenue by anticipating customer demand. The time-series analysis techniques are employed, such as autoregressive integrated moving average (ARIMA), seasonal autoregressive integrated moving average + exogenous variables (SARIMAX) and exponential smoothing (ETS) models, to analyze historical top-up data. The data is pre-processed, identified patterns, and trained the model to forecast future top-up frequencies. The model's predictions closely aligned with the actual data, indicating its effectiveness. The ARIMA model produced reliable forecasts for short-term top-up frequencies, showing the best performance overall with the lowest RMSE and MAE, and a positive R-squared indicating some ability to explain the data. SARIMAX and EST both perform poorly, with very high RMSE and MAE, and negative R-squared values. The results demonstrated that time-series analysis could enhance decision-making in the telecommunications industry by enabling better forecasting of customer behavior. This helps in optimizing marketing strategies, inventory management, and service availability.

Navigating OpenDOAR Distribution: A Deep Dive into Predictive Modeling and Trend Forecasting

Effective navigation and strategic foresight are imperative in the dynamic realm of academic repositories. This study aims to comprehensively understand global scholarly communication dynamics by analyzing repository distribution and forecasting future trends using predictive modeling methodologies. Employing a mixed-methods approach, this research blends quantitative analysis and predictive modeling techniques. It encompasses phases such as distribution analysis of repositories across continents, examination of repository counts across countries using GeoPandas and Matplotlib, optimization of Long short-term memory (LSTM) networks, application of Random Forest Regressor models, and time series analysis using Autoregressive Integrated Moving Average (ARIMA) models. The study reveals profound disparities in repository counts across continents, reflecting variations in research infrastructure. Visualizations vividly depict geographical patterns and disparities in repository distribution. Predictive modeling techniques offer insights into future trends in repository development, empowering decision-makers with strategic insights. While this study provides valuable insights, limitations exist, such as the scope limited to OpenDOAR repositories and the need for further refinement in predictive modeling techniques; future research should explore additional factors influencing repository development.

Hybrid Long Short-Term Memory Wavelet Transform Models for Short-Term Electricity Load Forecasting

To ensure the constant availability of electrical energy, power companies must consistently maintain a balance between supply and demand. However, electrical load is influenced by a variety of factors, necessitating the development of robust forecasting models. This study seeks to enhance electricity load forecasting by proposing a hybrid model that combines Sorted Coefficient Wavelet Decomposition with Long Short-Term Memory (LSTM) networks. This approach offers significant advantages in reducing algorithmic complexity and effectively processing patterns within the same class of data. Various models, including Stacked LSTM, Bidirectional Long Short-Term Memory (BiLSTM), Convolutional Neural Network—Long Short-Term Memory (CNN-LSTM), and Convolutional Long Short-Term Memory (ConvLSTM), were compared and optimized using grid search with cross-validation on consumption data from Lome, a city in Togo. The results indicate that the ConvLSTM model outperforms its counterparts based on Mean Absolute Percentage Error (MAPE), Root Mean Squared Error (RMSE), and correlation coefficient (R2) metrics. The ConvLSTM model was further refined using wavelet decomposition with coefficient sorting, resulting in the WT+ConvLSTM model. This proposed approach significantly narrows the gap between actual and predicted loads, reducing discrepancies from 10–50 MW to 0.5–3 MW. In comparison, the WT+ConvLSTM model surpasses Autoregressive Integrated Moving Average (ARIMA) models and Multilayer Perceptron (MLP) type artificial neural networks, achieving a MAPE of 0.485%, an RMSE of 0.61 MW, and an R2 of 0.99. This approach demonstrates substantial robustness in electricity load forecasting, aiding stakeholders in the energy sector to make more informed decisions.

Forecasting the export competitiveness of Peruvian coffee in major importing countries usingautoregressive integrated moving average modeling

The study analyzes the export competitiveness of Peruvian coffee in international markets using the ARIMA model to forecast export trends. The objective is to identify factors influencing the sector's profitability and sustainability. Time series of export volumes and prices were evaluated, revealing that the ARIMA model allows for accurate predictions of future trends. The results suggest that the Peruvian coffee sector faces challenges related to international price fluctuations and global competition. Emphasis is placed on the importance of efficiency in production and export processes to maintain and enhance the competitiveness of Peruvian coffee in the global market.

Application of ITransformers to Predicting Preterm Birth Rate. Comparison with the ARIMA Model

Application of ITransformers to Predicting Preterm Birth Rate. Comparison with the ARIMA Model

Trajectory of suicide among Indian children and adolescents: a pooled analysis of national data from 1995 to 2021

BackgroundSuicide is a major public health concern in India especially among children and adolescents. The yearly national statistics show a concerning trend of rising suicide deaths in these age groups.MethodsThe present study, taking 26 years of national data from the National Crime Record Bureau during 1995–2021, examined the trend, patterns, means, and modes of children/adolescent suicides in India. We also undertook a time series analysis by using ARIMA (0,2,1) model to forecast the expected suicide rate for the next one decade.ResultsA rising trend of suicide rate among children and adolescents was observed in India over the last 26 years. The forecast indicates a continuance of rising suicide cases for the upcoming decade in India. A substantially different trend of suicide rate was observed among early and late adolescents indicating significantly high vulnerability of late adolescents. Among children /adolescents, the most common causes of suicide were family problems, academic failure, illness, and unemployment. Illness has emerged as one of the leading causes of suicide, with a significant rise over time. Poverty and unemployment were also found as the important contributors with a steadily increasing trend of suicide among children and adolescents facing these problems in recent years.ConclusionThe study provides important analysis and information on suicide among children/adolescents in India, by providing useful insights for parents, teachers, policymakers, healthcare practitioners, and stakeholders aiming to prevent and control children and adolescent suicide and boost mental health. The study also provides important leads on risk factors with a forecast of suicide trends for the next 10 years.

Comparative time series analysis of Anatolian water buffalo stock in Türkiye

Water buffalo holds an important place in the livestock heritage in Türkiye. In the current study, buffalo numbers between 2023 and 2032 were estimated by using buffalo number data from 1991 to 2022. In the study, FAOSTAT data between 1961 and 2021 and TÜİK data for 2022 were used to obtain the time series. ARIMA modeling method was used in the study conducted in R programming language. ARIMA is a time series model that can make predictions from time series based on explaining univariate data. According to the results of the analysis made in the study, it is seen that the number of buffaloes in Türkiye will continue to decrease. According to the results obtained, it is estimated that the number of buffaloes will be 157,755 heads in 2023, 96,851 heads in 2027 and 19,136 heads in 2032. The disease resistance of buffaloes, their high adaptability, the high demand for water buffalo products, the preference for high-yielding cows for milk production despite government support payments, increasing mechanization, the reduction of buffalo habitats and the increase in input costs have led to a decline in Anatolian water buffalo populations. To prevent the Anatolian water buffalo from being threatened with extinction, the population decline must be halted and improved. To ensure this, the decline of the pastures that form the buffalo’s habitat must be halted, pasture areas must be increased, and existing pastures rehabilitated, government subsidies for buffalo and calf rearing must be increased and market opportunities for buffalo products must be expanded.

Analysis and Prediction of Atmospheric Environmental Quality Based on the Autoregressive Integrated Moving Average Model (ARIMA Model) in Hunan Province, China

Based on the panel data of atmospheric environmental pollution in Hunan Province from 2016 to 2023, the autoregressive integrated moving average model (ARIMA) is introduced to evaluate and predict the current status of atmospheric environmental quality in Hunan Province of China, and the constructed ARIMA model has an excellent prediction effect on the atmospheric environmental quality in Hunan Province. The following conclusions are obtained through the prediction and analysis based on the ARIMA model: (1) the atmospheric environmental quality in Hunan Province shows a year-on-year improvement trend; (2) the ARIMA model prediction method is reliable and effective and can accurately analyze and predict the concentrations of air pollutants (PM2.5, PM10, SO2, and CO) and atmospheric environmental quality, and the prediction results show that the outdoor air quality of Hunan Province will improve gradually each year from 2024 to 2028; (3) this study contributes a better understanding of the ambient air quality in Hunan Province during 2016–2023 and provides good forecasting results for air pollutants during the period of 2024–2028.

Olive Oil as a Tourist Attraction: An Analysis of Demand

Olive oil is not only an essential culinary element in the diet of Mediterranean countries, but it is also becoming an increasingly attractive element from a tourism perspective. The olive grove landscape, ancient olive trees, and oil mills are increasingly attracting tourists eager to experience this type of tourism. This research analyzes the profile of the oleotourist in the four designations of the province of Córdoba (Spain). SARIMA models have been applied to predict oleotourism demand, resulting in a growing demand, which will create jobs in the towns dedicated to this cultivation. The main problem is the seasonality of this demand, which can be corrected by creating complementary activities such as olive oil festivals, cooking workshops, or tasting sessions. A logit model has also been developed to determine which variables influence the probability of being satisfied with the oleotourism experience, with environmental conservation and travel cost being the most influential. Regarding the bivariate analysis, motivation and satisfaction with the trip are closely related, as is the personal classification of the oleotourist; the more they know about the world of olive cultivation, the more satisfied they are with their experience.

Using ARIMA and ETS models for forecasting water level changes for sustainable environmental management

It is vital to provide useful hydrological forecasts for urban and agricultural water management, hydropower generation, flood protection and management, drought mitigation and alleviation, and river basin planning and management, among other things. This paper introduces a simple and flexible hydrological time series forecasting framework. Predicting water levels is crucial, given the need for sustainable environmental management. The prognosis should be reasonable to persuade individuals to take proper precautions. While many methods have been developed to predict water levels, here the effectiveness of two approaches to predicting river water levels was assessed. For this purpose, nine years of data were used, which were divided into input data (2014–2021) and validation data (2022), on water levels in the Morava e Binçës river for the Vitia station, in the form of monthly time series records, to identify the best model among those used and to identify the information necessary for water resource management and hazard control. Models Autoregressive Integrated Moving Average(ARIMA) and Error Trend and Seasonality, or Exponential Smoothing (ETS), were examined using the R package to determine the most accurate. The results indicate the applicability of both models, as evidenced by the Root Mean Square Error (RMSE) and the Mean Absolute Error (MAE). The predictive analysis based on historical water levels allowed the identification of distinct periods characterized by high and low water levels between 2022 and 2024, which is important for the area in question due to the numerous flood events occurring here.

Time Series Forecasting of Emission Trends Using Recurrent Neural Networks

This paper explores the application of Recurrent Neural Networks (RNNs) for forecasting emission trends, a critical aspect of addressing climate change and formulating effective environmental policies. Traditional forecasting methods, such as Autoregressive Integrated Moving Average (ARIMA) and Exponential Smoothing, often fail to capture the complex nonlinear relationships and temporal dependencies inherent in emission data. In contrast, RNNs, particularly Long Short-Term Memory (LSTM) networks, are designed to recognize patterns in sequential data, making them well-suited for time series forecasting tasks. This study employs a comprehensive methodology that includes data collection from reputable sources, feature selection, RNN model design, and rigorous evaluation using various performance metrics. The results indicate that RNNs significantly outperform traditional forecasting techniques in terms of accuracy, providing valuable insights into future emission trajectories. The findings underscore the potential of RNNs as powerful tools for policymakers and researchers, facilitating more informed decision-making in the fight against climate change.

A denoising algorithm based on ARIMA and competitive K-SVD for the diagnosis of rolling bearing faults

Rolling bearings are extensively employed in industrial production as essential support components for rotating machinery. However, under conditions of high load and harsh operation, bearings are highly susceptible to failure. The weak vibration signals associated with these failures may be obscured by complex harmonic interference and strong noise, posing challenges for the accurate diagnosis of rolling bearing failures. In this paper, an autoregressive integrated moving average and competitive K-singular value decomposition (ARIMA-CK-SVD) algorithm is proposed to realize effective extraction of faulty pulse signals in a strong interference environment. First, the ARIMA model is used to preprocess the original signal to eliminate the interference of harmonic components. Second, a method is proposed for the adaptive selection of parameters in the ARIMA model, with consideration given to the characteristics of K-SVD. Subsequently, a competitive mechanism is introduced during the dictionary update phase of the algorithm to adjust the pattern of atomic updates and eliminate noise atoms. Finally, the effectiveness of the ARIMA-CK-SVD has been validated through simulation experiments and engineering tests.