Inflation forecasting has been and continues to be an important issue for the world’s economies. Governments, through their central banks, watch closely inflation indicators make national decisions policies. This study proposes forecast rate in five Latin American emerging economies based on commonly used seasonal autoregressive integrated moving average (SARIMA) approach combined with long sho...

Accurate incidence forecasting of infectious disease is critical for early prevention and for better government strategic planning. In this paper, we present a comprehensive study of different forecasting methods based on the monthly incidence of typhoid fever. The seasonal autoregressive integrated moving average (SARIMA) model and three different models inspired by neural networks, namely, ba...

This paper describes the development of an empirical model to forecast epidemics of Ross River virus (RRV) disease using the multivariate seasonal auto-regressive integrated moving average (SARIMA) technique in Brisbane, Australia. We obtained computerized data on notified RRV disease cases, climate, high tide, and population sizes in Brisbane for the period 1985-2001 from the Queensland Depart...

This study aims at modelling Nigerian total livebirths data, and to select the appropriate model for the disaggregated livebirths series among the proposed univariate stochastic time series models, based on in-sample fitting. Forecast of demographic variables such as births has a great influence on the growth of a population with respect to its demands on various systems such as education, heal...

Analysing and modelling efforts on production throughput are getting more complex due to random variables in today’s dynamic production systems. The objective of this study is to take multiple random variables of production into account when aiming for production throughput with higher accuracy of prediction. In the dynamic manufacturing environment, production lines have to cope with changes i...

This paper proposes a hybrid methodology that exploits the unique strength of the seasonal autoregressive integrated moving average (SARIMA) model and the support vector machines (SVM) model in forecasting seasonal time series. The seasonal time series data of Taiwan’s machinery industry production values were used to examine the forecasting accuracy of the proposed hybrid model. The forecastin...

This paper aims to find a forecasting model based on the comparative study of wavelet- ARIMA and EMD-ARIMA models residual-based selection technique for temperature time series. Time series analysis is essential in studying data investigating variation predicting future trend, which we can control changes make good decisions. And most important understand trend with time. applied hybridized wav...

Most of today’s time series data contain anomalies and multiple seasonalities, accurate anomaly detection in these is critical to almost any type business. However, most mainstream forecasting models used for can only incorporate one or no seasonal component into their forecasts cannot capture every known pattern data. In this paper, we propose a new multi-seasonal model that extends the popula...