Search Results (5)

Background: Malaria remains a climate-driven public health issue in Burkina Faso, yet the interactions between climatic factors and malaria interventions across different zones are not well understood. This study estimates time delays in the effects of climatic factors on malaria incidence, develops forecasting models, and assesses their short-term forecasting performance across three distinct climatic zones: the Sahelian zone (hot/arid), the Sudano-Sahelian zone (moderate temperatures/rainfall); and the Sudanian zone (cooler/wet). Methods: Monthly confirmed malaria cases of children under five during the period 2015–2021 were analyzed using Bayesian generalized autoregressive moving average negative binomial models. The predictors included land surface temperature (LST), rainfall, the coverage of insecticide-treated net (ITN) use, and the coverage of artemisinin-based combination therapies (ACTs). Bayesian variable selection was used to identify the time delays between climatic suitability and malaria incidence. Wavelet analysis was conducted to understand better how fluctuations in climatic factors across different time scales and climatic zones affect malaria transmission dynamics. Results: Malaria incidence averaged 9.92 cases per 1000 persons per month from 2015 to 2021, with peak incidences in July and October in the cooler/wet zone and October in the other zones. Periodicities at 6-month and 12-month intervals were identified in malaria incidence and LST and at 12 months for rainfall from 2015 to 2021 in all climatic zones. Varying lag times in the effects of climatic factors were identified across the zones. The highest predictive power was observed at lead times of 3 months in the cooler/wet zone, followed by 2 months in the hot/arid and moderate zones. Forecasting accuracy, measured by the mean absolute percentage error (MAPE), varied across the zones: 28% in the cooler/wet zone, 53% in the moderate zone, and 45% in the hot/arid zone. ITNs were not statistically important in the hot/arid zone, while ACTs were not in the cooler/wet and moderate zones. Conclusions: The interaction between climatic factors and interventions varied across zones, with the best forecasting performance in the cooler/wet zone. Zone-specific intervention planning and model development adjustments are essential for more efficient early-warning systems. Full article

Short-time forecasting of the demand on water distribution networks is a challenging task because of the high variability and uncertainty of that demand. Of the different approaches used, we consider the probability modeling of demand time series to be the most interesting, and specifically propose the use of Generalized Autoregressive Moving Average (GARMA) models. The complete proposed model uses a gamma probability density function, variables for weekends, and harmonic functions for daily and weekly seasonality, among other parameters. In the context of the Battle of Water Demand Forecasting, we train and test the model with a demand database for ten District Metered Areas. We obtain high accuracy, with mean absolute error values of around 0.25 L/s to 1.89 L/s. Full article

The aim of this paper is to assess the significant impact of using quantile analysis in multiple fields of scientific research . Here, we focus on estimating conditional quantile functions when the errors follow a GARMA (Generalized Auto-Regressive Moving Average) model. Our key theoretical contribution involves identifying the Quantile-Regression (QR) coefficients within the context of GARMA errors. We propose a modified maximum-likelihood estimation method using an EM algorithm to estimate the target coefficients and derive their statistical properties. The proposed procedure yields estimators that are strongly consistent and asymptotically normal under mild conditions. In order to evaluate the performance of the proposed estimators, a simulation study is conducted employing the minimum bias and Root Mean Square Error (RMSE) criterion. Furthermore, an empirical application is given to demonstrate the effectiveness of the proposed methodology in practice. Full article

The Gegenbauer autoregressive moving-average (GARMA) model is pivotal for addressing non-additivity, non-normality, and heteroscedasticity in real-world time-series data. While primarily recognized for its efficacy in various domains, including the health sector for forecasting COVID-19 cases, this study aims to assess its performance using yearly sunspot data. We evaluate the GARMA model’s goodness of fit and parameter estimation specifically within the domain of sunspots. To achieve this, we introduce the random coefficient generalized autoregressive moving-average (RCGARMA) model and develop methodologies utilizing conditional least squares (CLS) and conditional weighted least squares (CWLS) estimators. Employing the ratio of mean squared errors (RMSE) criterion, we compare the efficiency of these methods using simulation data. Notably, our findings highlight the superiority of the conditional weighted least squares method over the conditional least squares method. Finally, we provide an illustrative application using two real data examples, emphasizing the significance of the GARMA model in sunspot research. Full article

This paper aims to evaluate the forecast capability of electricity markets, categorized by numerous major characteristics such as non-stationarity, nonlinearity, highest volatility, high frequency, mean reversion and multiple seasonality, which give multifarious forecasts. To improve it, this investigation proposes a new hybrid approach that links a dual long-memory process (Gegenbauer autoregressive moving average (GARMA) and generalized long-memory GARCH (G-GARCH)) and the empirical wavelet transform (EWT) and local linear wavelet neural network (LLWNN) approaches, forming the k-factor GARMA-EWLLWNN model. The future hybrid model accomplished is assessed via data from the Polish electricity markets, and it is matched with the generalized long-memory k-factor GARMA-G-GARCH process and the hybrid EWLLWNN, to demonstrate the robustness of our approach. The obtained outcomes show that the suggested model presents important results to define the relevance of the modeling approach that offers a remarkable framework to reproduce the inherent characteristics of the electricity prices. Finally, it is presented that the adopted methodology is the most appropriate one for prediction as it realizes a better prediction performance and may be an answer for forecasting electricity prices. Full article