The objective of the study was to forecast value oil and gas exports in Indonesia using ARIMA Box-Jenkins. With this prediction, it is hoped that can be a for future policy making. This export data obtained from Indonesian Central Bureau Statistics (BPS) website, raw January 2010 March 2022. predicted method with help R software. stages analysis include: stationary test, build model indication,...

The demostration will center on the application of program TSW to a large set of monthly time series. TSW is a Windows interface of updated versions of programs TRAMO (Time series Regression with Arima noise, Missing values, and Outliers) and SEATS (Signal Extraction in ARIMA Time Series). The program estimates a general regression-ARIMA model, and computes forecasts and interpolators for possi...

An in-depth look at the Auto_ARIMA function and its constituents with an application to financial data. Visual Numerics, Inc., makes no warranty of any kind with regard to this material, included, but not limited to, the implied warranties of merchantability and fitness for a particular purpose. Visual Numerics, Inc., shall not be liable for errors contained herein or for incidental, consequent...

This paper attempts to compare the forecasting performance of the ARIMA model and hybrid ARMA-GARCH Models by using daily data of the Iran’s exchange rate against the U.S. Dollar (IRR/USD) for the period of 20 March 2014 to 20 June 2015. The period of 20 March 2014 to 19 April 2015 was used to build the model while remaining data were used to do out of sample forecasting and check the forecasti...

Machine learning (ML) models, including artificial neural networks (ANN), generalized regression (GRNN), and adaptive neuro-fuzzy interface systems (ANFIS), have received considerable attention for their ability to provide accurate predictions in various problem domains. However, these models may produce inconsistent results when solving linear problems. To overcome this limitation, paper propo...

It is meaningful and of certain theoretical value for the development economy through analyzing fluctuation rules international oil prices forecasting future trend prices. By composing autoregressive integrated moving average (ARIMA) model combination model-generalized conditional heteroskedasticity (ARIMA-GARCH) prices, study shows that ARIMA (1,1,0)-GARCH (1,1) more suitable short-term with h...

This study aimed to identify circulating influenza virus strains and vulnerable population groups and investigate the distribution and seasonality of influenza viruses in Ningbo, China. Then, an autoregressive integrated moving average (ARIMA) model for prediction was established. Influenza surveillance data for 2006-2014 were obtained for cases of influenza-like illness (ILI) (n = 129,528) fro...