English (United Kingdom)

https://curated-unify.zendy.io/wp-json/zendy-region/v1/featured_content/oa?rat=en

https://curated-unify.zendy.io/wp-json/zendy-region/v1/highlighted_journal/

Global: Zendy Plus annual

Presents the access of premium content as premium feature

Premium Content

Presents the keyphrase highlighting as premium feature

Keyphrase Highlighting

Presents the summarisation as premium feature

Summarisation

Insights

Presents the pdf analysis as premium feature

PDF Analysis

Presents the zaia usage as premium feature

ZAIA

Global: Zendy Tools annual

Global: Zendy Free Plan

Global: Zendy Tools monthly

Global: Zendy Plus monthly

Time series analysis is considered one of the most important analysis processes at the present time, especially if it is a multivariate analysis. This analysis helps the decision maker in making his future decision based on the behavior of a phenomenon in the past. This is done for many economic, financial, engineering, medical, and other important fields. So we were keen in this article to address a multivariate time series using the vector autoregressive models analysis of the practical time series This analysis is also used in the process of forecasting the future of multiple time series. Three packages from the R program, are used for numerical analysis these data, that are "vars", "MTS(VAR)", and "forecast" packages. For these data ARIMA (0,1,0) model is the best model for non-stationary time series, but the best model is ARIMA(0,0,0) with zero mean for stationary time series. VAR(1) model is better than VAR(2) model for analyzing the data. Also, VAR(1) model is better than VAR(2) model for identifying the Lags order. Comparing between VAR(p) and VARS(p), we found that the VAR(p) model order is better than the VARS(p) model order for identifying lag order. The significance predictor time series is Institutional investor, the other predictors are non-significance. No serial correlation in error terms. The Institutional investor and Trade values time series are useful for forecasting other time series. Only the Exchange rate time series has instantaneous causality between it and other time series. Finally, A five forecasts values of all stationary time series, using VARS(1) models, are predicted.

Forecasting EGX30 index time series using vector autoregressive models VARS