ARIMA Method

The ARIMA method uses historical values to set the parameters described above. These datasets can only be used when they have specific properties like homoscedasticity and uniformity. The datasets of inflation do not have these properties, therefore some deformations on the dataset need to be performed. The number of deformations needed is described by the letter d. The theory speaks of ARIMA (p,d,q) models to describe the lag structure. In this research, different ARIMA lag structures are used to model the different indexes. The choices of the p d and q are based on the Akaike Information Criterion (AIC). The model-hng is executed with Palisade s RISK software as plug-in in Microsoft Excel. 

A general approach was developed by G.E.P. Box and G.M. Jenkins (S) which combines these various methods into an analysis which permits choice of the most appropriate model, checks the forecast precision, and allows for interpretation. The Box-Jenkins analysis is an autoregressive integrated moving average model (ARIMA). This approach, as implemented in the MINITAB computer program is one used for the analyses reported here. 

While it has been well documented that inorganic phosphate depresses production of many antibiotics, it has also been shown to stimulate or enhance the production of others. The first reported use of high levels of inorganic phosphate for antibiotic production was in 1964, when Arima reported the production of pyrrolnitrin from Pseudomonas fluorescens [96]. This antidermatophytic antibiotic was discovered when an accidental addition of 10 times as much inorganic phosphate as intended subsequently resulted in not only higher production of pyrrolnitrin, but more reproducible results than previous methods had yielded. The production of globopeptin was increased 2.5 fold by addition of magnesium phosphate [97]. 

The results presented in this section show that the use of advanced forecasting methods leads to the reduction of Bullwhip Effect. Thus, the inclusion of ARIMA models at the lowest level of the supply chain provides very interesting results, and it can significantly reduce, in many cases, the Bullwhip Effect. In these circumstances, we... 

To develop the tool, we have considered only simple forecasting methods, such as moving averages and exponential smoothing, so that each level of the chain uses the best one that suits the demand it should deal with. With them, it is possible to achieve great results in reducing Bullwhip Effect. Even so, we have also shown that the inclusion of more advanced forecasting methods (ARIMA models) allows an even better system performance. 

The interacting boson model applies group theoretical (or algebraic) methods and describes nuclear states of various collectivity and symmetry in a uniform framework (see reviews by lachello and Arima (1987), lachello and Van Isacker (1991), and Fenyes (2002)). 

J. Murata, S. Sadakuni, K. Yagi, Y. Sano, T. Okamoto, K. Arima, A.N. Hattori, H. Mimura, K. Yamauchi, Planarization of GaN(OOOl) surface by photo-electrochemical method with sohd acidic or basic catalyst, Jpn. J. Appl. Phys. 48 (2009) 121001. 

Kakihana M., Yoshimura M. Synthesis and characteristics of complex multicomponent oxides prepared by polymer complex method. Bull. Chem. Soc. Jpn. 1999a 72 1427-1443 Kakihana M., Arima M., Nakamura Y., Yashima M., Yoshimura M. Spectroscopic characterization of precursors used in the Pechini-type polymerizable complex processing of barium titanate. Chem. Mater. 1999b 11 438-450... 

In our further analysis we are going to develop this approach even more and complement it with other approaches like ARIMA or ARMA methods. 

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