Auto-Regressive Integrated Moving Average Model (ARIMA): conceptual and methodological aspects and applicability in infant mortality

Abstract

This reflective theoretical article, aims to discuss conceptual and methodological aspects about the applications of time series modeling, in particular, the Integrated Auto-regressive Moving Average model and its applicability in infant mortality. This modeling makes it possible to predict future values using past data, outlining and estimating possible scenarios of the health event, highlighting its magnitude. Due to the persistence of infant mortality as a public health problem, the applicability of this method is useful in the timely and systematic management of child health indicators, in addition to being a method with low operating cost, which in contexts of cost reduction in public healthcare services, becomes a potential management tool. However, there are still gaps in the use of statistical methods in the decision-making and policy-making process in public healthcare, such as the modeling in question. These are methodological (robust statistics), institutional (outdated information systems) and cultural obstacles (devaluation of the data produced, mainly at the local level).

ARIMA Modeling and Forecasting of National Consumer Price Index in Nepal

“…The AR part involves using past data points to predict future ones, the I component includes differentiating the data to achieve stationarity, and the MA aspect uses past forecast errors in the prediction model. This model is particularly effective in applications offering a cost-effective tool for planning and decision-making (Silva et al, 2021). The components of ARIMA are described as follows:…”

“…ARIMA modelling for forecasting the National Consumer Price Index (NCPI) in Nepal is a powerful instrument for deciphering and projecting inflationary patterns (Yunus et al, 2015). Despite its complexity and the need for meticulous consideration of multiple factors, this method plays a crucial role in aiding economic planning and policy development (Silva et al, 2021). The efficacy of the ARIMA model is contingent upon the precision of the data used, the stability of the economic environment, and the correctness in the specification of the model parameters (Singh et al, 2020).…”

“…The utilization of the Auto-Regressive Integrated Moving Average (ARIMA) model emerges as a valuable statistical tool for the anticipation of random values like Nepal's National Consumer Price Index. With its autoregressive, integrated, and moving average components, this approach facilitates predictions based on historical data and prevailing trends (Wikipedia, 2023;Silva et al, 2021). However, the efficacy of ARIMA in forecasting Nepal's National Consumer Price Index (NCPI) is contingent upon the data quality, economic stability, and the appropriateness of the model configuration (Singh et al, 2020;Yunus et al, 2015).…”

Temporal analysis of mortality from preventable causes in the first 24 hours of life, 2000-2021

“…Analyzing temporal behavior and making predictions regarding infant mortality or its components is a tool with great potential in the field of public health, as it allows knowing the behavior of the phenomenon in question over time, therefore supporting decision making ( 15 ) . Different studies recognize the applicability of temporal analysis in understanding infant mortality ( 15 - 16 ) . A study conducted in the state of São Paulo showed a prospect of a drop in infant mortality in the period from 1996 to 2016 ( 16 ) .…”

Análisis temporal de la mortalidad por causas evitables en las primeras 24 horas de vida, 2000-2021

“…Analizar el comportamiento temporal y realizar predicciones acerca de la mortalidad infantil o sus componentes es una herramienta con gran potencial en el campo de la salud pública, ya que permite conocer el comportamiento del fenómeno en cuestión en el tiempo, contribuyendo a la toma de decisiones (15) . Diferentes estudios reconocen la aplicabilidad del análisis temporal en la comprensión de la mortalidad infantil (15)(16) . Un estudio realizado en el estado de São Paulo mostró una perspectiva de disminución de la mortalidad infantil en el período de 1996 a 2016 (16) .…”