Oil is one of the vital energy sources in a country. Indonesia countries that was once world's primary leading producer but currently threatened with an oil deficit. This condition certainly requires right strategy and policy so can meet its domestic needs. study aims to forecast Indonesia's consumption production for 2022-2026 using past data from 1980-2021. used Auto-Regressive Integrated Mov...

Traditionally, the autoregressive integrated moving average (ARIMA) model has been one of the most widely used linear models in time series forecasting. However, the ARIMA model cannot easily capture the nonlinear patterns. Support vector machines (SVMs), a novel neural network technique, have been successfully applied in solving nonlinear regression estimation problems. Therefore, this investi...

Proper models for prediction of time series data can be an advantage in making important decisions. In this study, we tried with the comparison between one of the most useful classic models of economic evaluation, auto-regressive integrated moving average model and one of the most useful artificial intelligence models, adaptive neuro-fuzzy inference system (ANFIS), investigate modeling procedur...

To model the trajectory of pandemic in Kuwait from February 24, 2020 to 28, 2021, we used two modeling procedures: Auto Regressive Integrated Moving Average (ARIMA) with structural breaks and Multivariate Adaptive Regression Splines (MARS), then mapped key breakpoints models set government-enforced interventions. The MARS model, as opposed ARIMA provides a more precise interpretation interventi...

Forecasting accuracy drives the performance of inventory management. This study is to investigate and compare different forecasting methods like Moving Average (MA) and Autoregressive Integrated Moving Average (ARIMA) with Neural Networks (NN) models as Feed-forward NN and Nonlinear Autoregressive network with eXogenous inputs (NARX). Data used to forecast is acquired from inventory database of...

In this paper an attempt is made to develop hybrid models using Artificial Neural Network (ANN) and Autoregressive Integrated Moving Average (ARIMA) for predicting the future exchange rate for US dollar. Simulation results of hybrid models were compared with results of ANN based models and ARIMA based models. Results show that the model ANN – ARIMA ANN gives a better performance than the other ...

Autoregressive integrated moving average (ARIMA) models are used in different researches for modelling and forecasting of traffic and Quality of Service (QoS) parameter values in telecommunication networks to make reasonable short, mediumand long-term predictions. We propose methodology to use ARIMA models for QoS prediction in network scenarios based on a preliminary detection and elimination ...

Abstract Drought stress under a changing climate can significantly affect agricultural production. Simulation of soil water dynamics in field conditions becomes necessary to understand changes develop irrigation guidelines. In this study, three models including Auto-Regressive Integrated Moving Average (ARIMA), Back-Propagation Artificial Neural Network (BP-ANN), and Least Squares Support Vecto...

BACKGROUND The infection rate of syphilis in China has increased dramatically in recent decades, becoming a serious public health concern. Early prediction of syphilis is therefore of great importance for heath planning and management. METHODS In this paper, we analyzed surveillance time series data for primary, secondary, tertiary, congenital and latent syphilis in mainland China from 2005 t...

Sugarcane industry is of crucial importance to the South Asian countries. These countries depend heavily on agriculture and sugarcane has immense potential contribute towards its economic development. Hence, precise timely forecast production concern for farmers, policy makers other stakeholders. In this manuscript, we strived growth rate important commodity using standard statistical approache...