Model identification is an important and complicated step within the autoregressive integrated moving average (ARIMA) methodology framework. This step is especially difficult for integrated series. In this article first investigate Box-Jenkins methodology and its faults in detecting model, and hence have discussed the problem of outliers in time series. By using this optimization method, we wil...

In this paper, forecasting of monthly electricity load using Box-Jenkins methodology and feed forward neural networks is discussed. This study investigates application of neural networks models and the results of neural networks will be compared with those obtained by Box-Jenkins method.

In statistical field Box-Jenkins Time Series is a linear method widely used to forecasting. The linearity makes the method inadequate to forecast real time series, which could present irregular behavior. On the other hand, in artificial intelligent field FeedForward Artificial Neural Networks and Continuous Machine Learning Systems are robust handlers of data in the sense that they are able to ...

The economic dimension has revealed that weak growth is one of the causes unemployment and related to GDP. Forecasting rate essential an important determinant monetary policy decisions needs be addressed. This study conducted identify whether novel coronavirus 2019, COVID-19 affects Malaysia’s forecast for next two years. Box-Jenkins approach uses Augmented Dickey-Fuller test stabilize data. Af...

Microbial Source Tracking: Current Methodology and Future Directions† Troy M. Scott, Joan B. Rose, Tracie M. Jenkins, Samuel R. Farrah, and Jerzy Lukasik* College of Marine Science, University of South Florida, St. Petersburg, Florida 33701; Department of Microbiology and Cell Science, University of Florida, Gainesville, Florida 32611; and Biological Consulting Services of North Florida, Inc., ...

The aim of this paper is to prove the validity of an alternative prediction technique to another classical one, which is Box-Jenkins methodology, in order to produce multivariate prediction. In particular, one-step ahead forecasts will be obtained for two time series: thermic and hydraulic power production. These forecasts are based on the past values of those series.